Diagnostic Biomarkers Research Articles

Inflammation-Associated Long Non-Coding RNAs (lncRNAs) in Chronic Viral Hepatitis- Associated Hepatocellular Carcinoma.

This study aimed to identify the expression profile and prognostic significance of inflammation-associated lncRNAs in chronic viral hepatitis (CVH) and CVH-associated hepatocellular carcinoma (CVH-HCC). In the first step, lncRNA expression analysis was performed by real-time polymerase chain reaction (RT-PCR) using an array panel of 84 inflammation-associated lncRNAs in 48 formalin-fixed paraffin-embedded (FFPE) tissue samples (12 CVH-HCC, 12 peritumoral cirrhotic parenchyma, 12 nontumoral cirrhotic CVH parenchyma, 12 normal liver samples). In the second step, 7 lncRNAs (DLEU2, HOTAIR, LINC00635, LINC00662, RP11-549J18.1, SNHG16 and XIST) were chosen for RT-PCR assay testing in 72 samples (24 CVH-HCC, 24 peritumoral cirrhotic parenchyma, 24 nontumoral cirrhotic CVH parenchyma samples). Fifty-six inflammation-associated lncRNAs were significantly up-regulated in the peritumoral cirrhotic parenchyma compared to the normal liver. Expression of 71 lncRNAs was significantly higher in peritumoral cirrhotic parenchyma compared to cirrhotic CVH parenchyma. DLEU2 and SNHG16 were up-regulated both in the tumor and peritumoral cirrhotic parenchyma compared to cirrhotic CVH parenchyma. Expression of LINC00662 was significantly higher in CVH-HCC than in cirrhotic CVH parenchyma. Expression of XIST was also increased in both tumor and peritumoral parenchyma samples, albeit without statistical significance. No significant association was found between lncRNA expressions and survival. Inflammation-associated lncRNAs DLEU2, SNHG16, LINC00662, and XIST are candidate diagnostic biomarkers in CVH-HCC. More evidence is needed to prove their utility as prognostic markers.

From Proteomics to Diagnosis: Biomarker Discovery in Tuberculosis Research.

Tuberculosis (TB) is a leading cause of death from a single infectious disease worldwide. Early and accurate diagnosis is advantageous for timely detection and prompt treatment, thereby reducing the risk of disease transmission, which is essential for effective TB control. Biomarkers provide a valuable resource for TB diagnosis. Proteomic technologies have emerged as a powerful tool in biomarker discovery. In this perspective, we explore how proteomic technologies contribute to the discovery of TB diagnostic biomarkers. We also address the challenges and discuss prospective methods to augment the performance of biomarkers in diagnosing TB.

Bioinformatics analysis and experimental validation of C6orf120 as a potential prognostic marker and therapeutic target for liver hepatocellular carcinoma.

The C6orf120 gene is a novel gene whose function has not been fully defined. Previous studies have associated it with various liver pathologies, but its specific role in hepatocellular carcinoma (LIHC) remains unclear. This study aimed to investigate the diagnostic and prognostic value of C6orf120 in LIHC, as well as its potential biological functions. In this preliminary research, we utilized data from various databases and bioinformatics tools, including TCGA, GEO, TIMER2, HPA, GEPIA, Linkeomics, Metascape, CIBERSORT, TargetScan, DIANA-microT, RNAinter, and ENCORI, to analyze the expression patterns and mechanisms of C6orf120 in LIHC. Our bioinformatics analysis revealed that C6orf120 is upregulated in LIHC and may serve as a diagnostic and prognostic biomarker. The aberrant expression of C6orf120 in LIHC was further supported by clinical samples and cell lines. In vitro experiments demonstrated that the knockdown of C6orf120 in HepG2 cells significantly reduced migration capacity without affecting proliferation. Additionally, the downregulation of C6orf120 in LIHC cells appeared to inhibit endothelial cell migration and angiogenesis, which are critical in tumorigenesis and development. In conclusion, our findings suggest that C6orf120 could serve as a novel diagnostic and prognostic biomarker for LIHC and is expected to be a prognostic marker and a potential therapeutic target in the clinical management of LIHC.

Unveiling ficolins: diagnostic and prognostic biomarkers linked to the Tumor Microenvironment in Lung Cancer

BackgroundFicolins (FCNs) are a family of proteins, comprising FCN1, FCN2 and FCN3, and integral to the immune system which have been implicated in the onset and progression of tumors. Despite their recognized roles, a comprehensive analysis of FCNs in lung cancer remains elusive.MethodsWe employed a variety of bioinformatics tools, including UCSC, SangerBox, Ualcan, cBioPortal, String, Metascape, GeneMANIA, TIDE, CTD, and CAMP databases to investigate the differential expression, diagnostic and prognostic significance, genetic alterations, functional enrichment, immune infiltration, and potential immunotherapeutic implications of FCN1, FCN2, and FCN3 in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Additionally, RT-qPCR and immunohistochemistry were utilized to validate the expressions of FCNs at the mRNA and protein levels in LUSC and LUAD.ResultsOur comprehensive bioinformatic analysis, supported by RT-qPCR and immunohistochemistry, revealed that the expressions of FCN1, FCN2 and FCN3 were consistently downregulated in both LUSC and LUAD tumor tissues. FCNs demonstrated significant diagnostic potential for LUSC and LUAD, with the area under the receiver operating characteristic curve (AUC) for FCN1 and FCN3 exceeding 0.90. Furthermore, FCN2 and FCN3 showed a strong negative correlation with overall survival (OS) in LUSC, whereas FCN1 and FCN2 were positively correlated with OS in LUAD, suggesting their prognostic value in lung cancer. Gene enrichment analysis indicated that FCNs were predominantly associated with the complement system and complement activation pathways. Immune infiltration analysis further revealed a significant positive correlation between FCNs and the presence of neutrophils and resting mast cells. Our analysis of immunotherapy outcomes revealed a significant disparity in the immunophenoscore (IPS) among lung cancer patients treated with immune checkpoint inhibitors (ICIs), distinguishing those with high FCN expression from those with low FCN expression. Additionally, we identified small molecule compounds related to FCNs and drugs pertinent to LUSC and LUAD.ConclusionFCNs held promise as diagnostic and prognostic biomarkers for LUSC and LUAD. This study also elucidated the relationship of FCNs with the tumor microenvironment, offering novel insights into the immunotherapeutic landscape for LUSC and LUAD.

Identification and Verification of Potential Markers Related to Myocardial Fibrosis by Bioinformatics Analysis.

Mounting evidence indicates that myocardial fibrosis (MF) is frequently intertwined with immune and metabolic disorders. This comprehensive review aims to delve deeply into the crucial role of immune-related signature genes in the pathogenesis and progression of MF. This exploration holds significant importance as understanding the underlying mechanisms of MF is essential for developing effective diagnostic and therapeutic strategies. The dataset GSE9735 about myocardial fibrosis and non-fibrosis was downloaded from GEO database. Differentially expressed genes (DEGs) were identified by 'limma' package in R software. Then, the biological function of DEG was determined by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. XCell was used to estimate the composition pattern of matrix and immune cells. Protein-protein interaction (PPI) network was constructed based on STRING analysis software, and Hub genes were screened and functional modules were analyzed. The correlation between hub genes and immune cell subtypes was analyzed. Hub genes with |correlation coefficient|> 0.45 and p-value < 0.05 were used as characteristic biomarkers. Finally, the logistic regression model is used to verify the three markers in the training set and verification set (GSE97358 and GSE225336). A total of 635 DEGs were identified. Functional enrichment analysis shows that inflammation and immune response, extracellular matrix and structural remodeling play an important role in the pathological mechanism of MF. Immune cell infiltration analysis showed that immune cells (Plasma cells, Eosinophils, Chondrocytes and Th2 cells) significantly changed in MF pathological conditions. In PPI network analysis, IL1β, TTN, PTPRC, IGF1, ALDH1A1, CYP26A1, ALDH1A3, MYH11, CSF1R and CD80 were identified as hub genes, among which IL1β, CYP26A1 and GNG2 were regarded as immune-related characteristic markers. The AUC scores of the three biomarkers are all above 0.65, which proves that they have a good discrimination effect in MF. In this study, three immune-related genes were identified as diagnostic biomarkers of MF, which provided a new perspective for exploring the molecular mechanism of MF. This study takes a comprehensive approach to understanding the intricate relationship between myocardial fibrosis and immune metabolism. By identifying key immune-related biomarkers, this study not only reveals the molecular basis of myocardial fibrosis but also paves the way for the development of novel diagnostic tools and therapeutic strategies. These findings are critical for improving patient prognosis and may have broader implications for studying and treating other cardiovascular diseases associated with immune dysregulation.

Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning

BackgroundLiver metastasis is one of the primary causes of poor prognosis in colon adenocarcinoma (COAD) patients, but there are few studies on its biomarkers.MethodsThe Cancer Genome Atlas (TCGA)-COAD, GSE41258, and GSE49355 datasets were acquired from the public database. Differentially expressed genes (DEGs) between liver metastasis and primary tumor samples in COAD were identified by limma, and functional enrichment analysis were performed. MuTect2 and maftools were used to measure somatic mutation rates, while ADTEx was used to measure copy number variations (CNVs). The intersection of three machine learning methods, support vector machine (SVM), Random Forest, and least absolute shrinkage and selection operator (LASSO), is utilized to screen biomarkers, and their diagnostic performance is subsequently validated. The correlation between biomarkers and immune cells infiltration was analyzed by Spearman method.Results47 DEGs between liver metastasis and primary tumor samples in COAD were obtained, which were mainly enriched in the complement and coagulation, extracellular matrix (ECM), and peptidase regulator activity, etc. 38 out of 47 DEGs had mutations and exhibited a high frequency of CNV amplification or deletion. Furthermore, 3 biomarkers (MMP3, MAB21L2, and COLEC11) were screened, which showed good diagnostic performance. The proportion of multiple immune cells, such as B cells naive, T cells CD4 naive, Monocytes, and Dendritic cells resting, was higher in liver metastasis samples than that in primary tumor samples. Meanwhile, MMP3, MAB21L2, and COLEC11 exhibited an outstanding correlation with immune cells infiltration.ConclusionIn short, 3 biomarkers with good diagnostic efficacy were identified, providing a new perspective of therapeutic targets for liver metastasis in COAD.

Determination of FGFR1 Functions in Cytarabine Treatment of Acute Myeloid Leukemia Through Bioinformatics Analysis

Aim: Among the most prevalent subtypes of acute leukemia is acute myeloid leukemia (LAML). Consequently, it is essential to understand the molecular causes of LAML and find its predictive and diagnostic biomarkers. The aim of this study is to determine the molecular functions of fibroblast growth factor receptor 1(FGFR1) involved in LAML pathogenesis and its potential therapeutic effect for AML treatment. Methods: The molecular docking interaction of the Cytarabine with its target FGFR1 was examined. The Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2), and UALCAN tools database were used to obtain the LAML gene expression datasets. Gene functional annotation was performed to investigate the DEGs' possible role. Using the Interactive Gene database retrieval tool (STRING) and a few chosen hub modules from the GeneMANIA database, the protein- protein interaction (PPI) network were constructed. A survival analysis was performed on the effects of hub genes on the overall survival of LAML patients. Results: As a result of docking, a strong interaction was observed between cytarabine and FGFR1. It has been discovered that cytarabine can reverse FGFR1 expression. The survival study results showed an association between the prognosis of AML patients and one of the central genes, FGFR1. Conclusion: The expression profile and functions of FGFR1 were determined in LAML patients. It has been shown that FGFR1 can be a viable therapeutic target for LAML and a possible biomarker for diagnosis.

Unveiling shared diagnostic biomarkers and molecular mechanisms between T2DM and sepsis: Insights from bioinformatics to experimental assays.

Septic patients with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, this study aimed to pinpoint potential genes related to sepsis with T2DM and develop a predictive model for the disease. The candidate genes were screened using protein-protein interaction networks (PPI) and machine learning algorithms. The nomogram and receiver operating characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blotting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine learning algorithms, demonstrating strong predictive capabilities. These biomarkers presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mouse models and blood samples were consistent with the findings of the bioinformatics analysis. The study elucidated the common molecular mechanisms associated with the pathogenesis of T2DM and sepsis and developed a gene signature-based prediction model for sepsis. These findings provide new targets for the diagnosis and intervention of sepsis complicated with T2DM.

Plasma-derived extracellular vesicles miR-335–5p as potential diagnostic biomarkers for fusion-positive rhabdomyosarcoma

BackgroundRhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma, with embryonal (ERMS) and alveolar (ARMS) representing the two most common histological subtypes. ARMS shows poor prognosis, being often metastatic at diagnosis. Thus, the discovery of novel biomarkers predictive of tumor aggressiveness represents one of the most important challenges to overcome and may help the development of tailored therapies. In the last years, miRNAs carried in extracellular vesicles (EVs), small vesicles of endocytic origin, have emerged as ideal candidate biomarkers due to their stability in plasma and their tissue specificity.MethodsEVs miRNAs were isolated from plasma of 21 patients affected by RMS and 13 healthy childrens (HC). We performed a miRNA profile using the Serum/Plasma Focus microRNA PCR panels (Qiagen), and RT-qPCR for validation analysis. Statistically significant (p < 0.05) miRNAs were obtained by ANOVA test.ResultsWe identified nine EVs miRNAs (miR-483-5p, miR-132-3p, miR-766-3p, miR-454-3p miR-197-3p, miR-335-3p, miR-17-5p, miR-486-5p and miR-484) highly upregulated in RMS patients compared to HCs. Interestingly, 4 miRNAs (miR-335-5p, miR-17-5p, miR-486-5p and miR-484) were significantly upregulated in ARMS samples compared to ERMS. In the validation analysis performed in a larger group of patients only three miRNAs (miR-483-5p, miR-335-5p and miR-484) were differentially significantly expressed in RMS patients compared to HC. Among these, mir-335-5p was significant also when compared ARMS to ERMS patients. MiR-335-5p was upregulated in RMS tumor tissues respect to normal tissues (p = 0.00202) and upregulated significantly between ARMS and ERMS (p = 0.04). Furthermore, the miRNA expression correlated with the Intergroup Rhabdomyosarcoma Study (IRS) grouping system, (p = 0.0234), and survival (OS, p = 0.044; PFS, p = 0.025). By performing in situ hybridization, we observed that miR-335-5p signal was exclusively in the cytoplasm of cancer cells.ConclusionWe identified miR-335-5p as significantly upregulated in plasma derived EVs and tumor tissue of patients affected by ARMS. Its expression correlates to stage and survival in patients. Future studies are needed to validate miR-335-5p as prognostic biomarker and to deeply elucidate its biological role.

MicroRΝΑ analysis in patients with myelodysplastic neoplasms. Possible implications in risk stratification

MiRNAs have been identified as participants in leukemogenesis by controlling several cellular functions, such as differentiation, proliferation, and apoptosis. Their role in myelodysplastic neoplasms (MDS) pathogenesis is researched due to implementations in early identification, classification, and therapeutical options. IPSS-R, being the most widely used MDS classification, underestimates early biological events that can alter the disease’s prognosis. The purpose of this study is to determine whether miRNA levels are aligned to MDS risk stratification groups and can therefore be used as diagnostic biomarkers. To evaluate miRNAs as possible biomarkers, we measured the levels of miR-181a-2-3p, miR-124-3p, miR-550a-3p, miR-155-5p, miR-151a-3p, and miR-125b-5p by a quantitative real-time PCR in bone marrow samples of 41 MDS patients. In conclusion, in myeloid malignancies, genomic characteristics may provide a wider apprehension of its clinical course and prognosis. MiRNAs constitute a possible diagnostic biomarker and therapeutic target, allowing intermediate-risk patients that express high levels of specific miRNAs to be re-classified and receive more advanced therapeutic agents. In our study, an association between high levels of miRNAs and worsening outcomes is established, supporting the need for further incorporation of molecular data into currently used classification systems.

Serum lncRNA ITGB2-AS1 and ICAM-1 as novel biomarkers for rheumatoid arthritis and osteoarthritis diagnosis.

The complete circulating long non-coding RNAs (lncRNAs) signature of rheumatoid arthritis (RA) and osteoarthritis (OA) is still uncovered. The lncRNA integrin subunit beta 2 (ITGB2)-anti-sense RNA 1 (ITGB2-AS1) affects ITGB2 expression; however, there is a gap in knowledge regarding its expression and clinical usefulness in RA and OA. This study investigated the potential of serum ITGB2-AS1 as a novel diagnostic biomarker and its correlation with ITGB2 expression and its ligand intercellular adhesion molecule-1 (ICAM-1), disease activity, and severity in RA and primary knee OA patients. Forty-three RA patients, 35 knee OA patients, and 22 healthy volunteers were included. Compared with healthy controls, serum ITGB2-AS1 expression was upregulated in RA patients but wasn't significantly altered in knee OA patients, whereas serum ICAM-1 protein levels were elevated in both diseases. ITGB2-AS1 showed discriminative potential for RA versus controls (AUC = 0.772), while ICAM-1 displayed diagnostic potential for both RA and knee OA versus controls (AUC = 0.804, 0.914, respectively) in receiver-operating characteristic analysis. In the multivariate analysis, serum ITGB2-AS1 and ICAM-1 were associated with the risk of developing RA, while only ICAM-1 was associated with the risk of developing knee OA. A panel combining ITGB2-AS1 and ICAM-1 showed profound diagnostic power for RA (AUC = 0.9, sensitivity = 86.05%, and specificity = 91.67%). Interestingly, serum ITGB2-AS1 positively correlated with disease activity (DAS28) in RA patients and with ITGB2 mRNA expression in both diseases, while ICAM-1 positively correlated with ITGB2 expression in knee OA patients. Our study portrays serum ITGB2-AS1 as a novel potential diagnostic biomarker of RA that correlates with disease activity. A predictive panel combining ITGB2-AS1 and ICAM-1 could have clinical utility in RA diagnosis. We also spotlight the association of ICAM-1 with knee OA diagnosis. The correlation of serum ITGB2-AS1 with ITGB2 expression in both diseases may be insightful for further mechanistic studies.

Lipidomics in Periodontal Disease Research: A Systematic Review.

Periodontal disease is a highly prevalent oral pathology in the human population, which has a significant local and systemic impact. Currently, multi-omics analyses, including lipidomics, are fundamental to obtaining an in-depth molecular understanding of the individual. Lipidomics is dedicated to the study of lipid species and their interactions in various health contexts. This specific multi-omics analysis is important for understanding the alteration of metabolism and signaling in disease, identifying biochemical markers, and potential therapeutic targets. This study aimed to carry out a systematic review of the existing scientific literature on lipidomics in periodontal disease and thus determine which molecules have already been analyzed and their potential in this specific disease. This study followed the recommendations of the PRISMA 2020 methodology. The inclusion criteria used were articles published in indexed journals between 2000 and 2023, written in English, and establishing an exclusive relationship about lipidomics in human periodontal disease. The articles were searched in three different databases. Considering the criteria defined, only six articles were selected and zed individually in detail. In four of the six studies, differences in the lipidome of individuals with periodontal disease were identified. Furthermore, phosphoethanolamine ceramide was found to have potential as a diagnostic biomarker. Finally, the therapeutic potential of a lipoxin A4 analogue was also identified. These results reinforce the need for future research in this area so that the consequences of this disease on the lipidome can be identified.

Assessment of combined serum sST2 and AFP levels in the diagnosis of hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality. Alpha-fetoprotein (AFP) is the most widely used diagnostic serum biomarker, but it still has limited accuracy in detecting HCC, suggesting the necessity of seeking more ideal biomarkers with high sensitivity and specificity. Soluble growth stimulation gene 2 (sST2) form of growth stimulating expression gene 2 (ST2), is expressed in various organs and can bind competitively to interleukin 33 (IL-33). Whether sST2 can serve as a serum biomarker for HCC is largely unknown. Objective To investigate the value of sST2 as a serum diagnostic marker for HCC. Methods This study included 93 newly diagnosed HCC patients (HCC group), 90 chronic hepatitis B patients (CHB group), and 90 healthy individuals (HCs group). Spearman correlation analysis was used to explore the relationships between sST2 and the experimental indicators in HCC group. The receiver operating characteristic (ROC) curve evaluated the efficacy of sST2 alone or in combination with AFP in the diagnosis of HCC. Result The median level of sST2 was significantly higher in HCC group (24.00 [15.20-49.90] ng/mL) compared to CHB group (19.55 [15.23-24.95] ng/mL) and HCs group (7.65 [5.20-10.53] ng/mL). No significant correlations were found between sST2 and other clinical indicators in HCC group. The Area Under Curve (AUC) of ROC curve to distinguish HCC patients from healthy controls and CHB group was 0.861 (sensitivity 82.80%, specificity 72.10%) and 0.709 (sensitivity 80.60%, specificity 52.50%), respectively. When combined with AFP, the AUC increased to 0.963 (sensitivity 82.90%, specificity 94.20%), and 0.895 (sensitivity 72.0%, specificity 100%), respectively. Conclusions The serum level of sST2 increased in HCC and its diagnostic performance is comparable to that of AFP, supporting its potential as a promising biomarker for detection of HCC. The combined use of sST2 and AFP enhances diagnostic efficacy for HCC.

TFE3 and TP53 were novel diagnostic biomarkers related to mitochondrial autophagy in chronic rhinosinusitis with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) belongs to a subtype of Chronic rhinosinusitis which is a heterogeneous inflammatory condition. It has been reported that mitophagy may provide a new therapeutic option for CRSwNP. The GSE136825 (training dataset) and GSE179265 (validation dataset) were scoured from the Gene Expression Omnibus database. The candidate genes related to mitophagy were identified by differential expression analysis. Subsequently, the biomarkers were selected from the machine learning, Receiver Operating Characteristic curves, and expression level verification. A backpropagation (BP) neural network was generated to evaluate the diagnostic ability of biomarkers. In addition, the infiltration abundance of immune cells, potential drugs, and related ear-nose-throat (ENT) diseases were analyzed based on the biomarkers. Finally, qPCR analysis was performed to verify these biomarkers. A total of 8 candidate genes were identified by overlapping 3,400 differentially expressed genes (DEGs) and 72 mitophagy-related genes Subsequently, TFE3 and TP53 were identified as biomarkers of CRSwNP, and the area under the curves (AUC) of the BP neural network was 0.74, which indicated that the biomarkers had excellent abilities. TFE3 and TP53 were co-enriched in the cancer pathway, cell cycle, endocytosis, etc. What's more, Macrophage and Immature dendritic cells had significant correlations with biomarkers. The drugs (Doxorubicin, Tetrachlorodibenzodioxin, etc.) and the ear-nose-throat diseases (hearing loss, sensorineural, tinnitus, etc.) related to biomarkers were predicted. Ultimately, qPCR results showed that the expression levels of TFE3 and TP53 in polyp tissue of CRSwNP were increased. Overall, TFE3 and TP53 could be used as biomarkers or potential therapeutic targets to diagnose and treat CRSwNP.

SB7-H4 is a diagnostic biomarker in epithelial ovarian cancer and correlates to platinum resistance.

Ovarian cancer, with its high mortality rate among gynecological cancers, is often diagnosed late due to the lack of early diagnostic symptoms and biomarkers. The tumor immune microenvironment has become a focal point in cancer diagnostic and therapeutic research. Among these, B7-H4, a checkpoint protein, plays a crucial role in immune regulation and tumor suppression, contributing to immune evasion within the tumor microenvironment. This study aims to identify the concentration of soluble B7-H4(sB7-H4) in the plasma of patients with ovarian cancer and to evaluate its clinical significance. Through a comprehensive analysis involving enzyme-linked immunosorbent assay, immunohistochemistry, and multicolor immunofluorescence, we quantified sB7-H4 levels in patient plasma and ascites, correlating these findings with tissue expression and clinical outcomes. Results indicated a strong association between high sB7-H4 levels and advanced disease, surgical outcomes, lymphatic metastasis, and platinum resistance. When compared with traditional biomarkers CA125 and HE4, sB7-H4, especially in conjunction with these markers, enhances the diagnostic accuracy for epithelial ovarian cancer, offering insights into disease progression and therapeutic efficacy. This comprehensive analysis suggests that sB7-H4 is a promising biomarker for EOC, providing valuable insights into diagnosis, stage differentiation, treatment effectiveness, and prognosis.

Comprehensive analysis identifies YKT6 as a potential prognostic and diagnostic biomarker in lung adenocarcinoma

BackgroundLung cancer is the most common cause of cancer-related death worldwide. The most prevalent histological subtype of lung cancer is lung adenocarcinoma (LUAD), with incidence rising each year. Treating LUAD remains a significant issue due to a lack of early diagnosis and poor therapy outcomes. YKT6 is a member of the SNARE protein family, whose clinical value and biological function in LUAD has yet to be established.MethodsTCGA, HPA and UALCAN were used to analyze YKT6 mRNA and protein levels, the correlation between YKT6 expression and clinicopathological features and prognosis. YKT6 mRNA and protein expression were verified by qRT-PCR, immunohistochemistry (IHC) and tissue microarrays (TMA). Additionally, lung cancer cell lines were chosen for YKT6 silencing to explore the effects on cell proliferation and migration. The cBioPortal was used to select YKT6-related genes. Protein-protein interaction (PPI) network was created based on STRING database and hub genes were screened, with their expression levels and prognosis values in LUAD analyzed accordingly. YKT6-related genes were enriched by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses.ResultsIn LUAD, YKT6 was distinctly highly expressed with relation to clinical features of staging, smoking, lymph node metastasis, and TP53 mutation. Elevated YKT6 expression was linked to adverse prognosis, serving as an independent unfavorable prognostic factor. Moreover, YKT6 presented high diagnostic value in LUAD patients (AUC = 0.856). Experimental validation indicated that freshly collected LUAD tissues showed significantly high mRNA expression of YKT6. IHC and TMA verified increased YKT6 protein level in LUAD. Knockdown of YKT6 inhibited cell proliferation and promoted apoptosis, with mitigated capability of migration and invasion. The top ten hub genes screened by PPI network were highly expressed in LUAD, and significantly associated with poor prognosis. GO and KEGG analyses showed that YKT6-related genes were mainly involved in cell cycle.ConclusionElevated YKT6 expression is related to poor prognosis of LUAD patients. YKT6 can serve as a novel biomarker for LUAD diagnosis and prognosis. Cell proliferation, migration and invasion was impaired with increased apoptosis upon YKT6 silencing in lung cancer cells. In summary, this study comprehensively uncovered that YKT6 could be identified as a potential prognostic and diagnostic biomarker in LUAD.

Correlating tissue and plasma‑specific piRNA changes to predict their possible role in pancreatic malignancy and chronic inflammation.

The aggressiveness of pancreatic ductal adenocarcinoma is primarily due to lack of effective early detection biomarkers. Circulating non-coding RNAs serve as diagnostic or prognostic biomarkers in multiple types of cancer. Comparison of their expression between diseased tissue and relevant body fluids such as saliva, urine, bile, pancreatic juice, blood etc. may reveal mechanistic involvement of common non-coding RNAs. piwi-interacting RNAs (piRNAs) are a class of non-coding RNAs. The aim of the present study was to investigate plasma and tumour tissue piRNA changes in patients with pancreatic cancer (PC) and explore the possible role in tumorigenesis and pancreatic inflammation. Sequencing of circulating plasma small RNAs from patients with PC and chronic pancreatitis (CP) was performed and differentially expressed piRNAs were compared with those in tissues. Subsequent search for target genes for those piRNAs was performed followed by pathway and cluster analysis. A total of 36 piRNAs were shown to be deregulated in pancreatic tumour tissue and alteration of 11 piRNAs was detected in plasma of patients with PC. piRNAs hsa-piR-23246, hsa-piR-32858 and hsa-piR-9137 may serve a key role in PC development as their expression was correlated in both plasma and tumour tissue. Key piRNA-target interactions interfering with key biological pathways were also characterized. A total of 19 deregulated piRNAs in plasma samples of patients with CP was identified; these targeted genes responsible for chronic inflammation. Therefore, the present study provides a comprehensive description of piRNA alteration in pancreatic malignancy and inflammation; these may be explored for biomarker potential in future.

Plasmonics-enhanced spikey nanorattle-based biosensor for direct SERS detection of mRNA cancer biomarkers.

We present a plasmonics-enhanced spikey nanorattle-based biosensor for direct surface-enhanced Raman scattering (SERS) detection of mRNA cancer biomarkers. Early detection of cancers such as head and neck squamous cell carcinoma (HNSCC) is critical for improving patient outcomes in regions with limited access totraditional diagnostic methods. Our method targets Keratin 14 (KRT14), a promising diagnostic mRNA biomarker for HNSCC, using a sandwich hybridization approach with magnetic beads and SERS spikey nanorattles (SpNR). We synthesized SpNR with a core-gap-shell structure to enhance SERS signals, achieving a limit of detection of 90 femtomolar. A pilot study using clinical samples demonstrated the efficacy of our biosensor in distinguishing between tissue with positive or negative diagnosis for HNSCC, highlighting its potential for rapid and sensitive cancer diagnostics in low-resource settings. This plasmonic assay offers a promising avenue for portable and high-specificity detection of nucleic acid biomarkers, with implications for early cancer detection and improved patient care, especially in middle and low-resource settings.

The human volatilome meets cancer diagnostics: past, present, and future of noninvasive applications.

Cancer is a significant public health problem, causing dozens of millions of deaths annually. New cancer screening programs are urgently needed for early cancer detection, as this approach can improve treatment outcomes and increase patient survival. The search for affordable, noninvasive, and highly accurate cancer detection methods revealed a valuable source of tumor-derived metabolites in the human metabolome through the exploration of volatile organic compounds (VOCs) in noninvasive biofluids. This review discusses volatilomics-based approaches for cancer detection using noninvasive biomatrices (breath, saliva, skin secretions, urine, feces, and earwax). We presented the historical background, the latest approaches, and the required stages for clinical validation of volatilomics-based methods, which are still lacking in terms of making noninvasive methods available and widespread to the population. Furthermore, insights into the usefulness and challenges of volatilomics in clinical implementation steps for each biofluid are highlighted. We outline the methodologies for using noninvasive biomatrices with up-and-coming clinical applications in cancer diagnostics. Several challenges and advantages associated with the use of each biomatrix are discussed, aiming at encouraging the scientific community to strengthen efforts toward the necessary steps to speed up the clinical translation of volatile-based cancer detection methods, as well as discussing in favor of (i) hybrid applications (i.e., using more than one biomatrix) to describe metabolite modulations that can be "cancer volatile fingerprints" and (ii) in multi-omics approaches integrating genomics, transcriptomics, and proteomics into the volatilomic data, which might be a breakthrough for diagnostic purposes, onco-pathway assessment, and biomarker validations.

New insights on genetic background of major diabetic vascular complications

BackgroundBy 2045, it is expected that 693 million individuals worldwide will have diabetes and with greater risk of morbidity, mortality, loss of vision, renal failure, and a decreased quality of life due to the devastating effects of macro- and microvascular complications. As such, clinical variables and glycemic control alone cannot predict the onset of vascular problems. An increasing body of research points to the importance of genetic predisposition in the onset of both diabetes and diabetic vascular complications.ObjectivesPurpose of this article is to review these approaches and narrow down genetic findings for Diabetic Mellitus and its consequences, highlighting the gaps in the literature necessary to further genomic discovery.Material and methodsIn the past, studies looking for genetic risk factors for diabetes complications relied on methods such as candidate gene studies, which were rife with false positives, and underpowered genome-wide association studies, which were constrained by small sample sizes.ResultsThe number of genetic findings for diabetes and diabetic complications has over doubled due to the discovery of novel genomics data, including bioinformatics and the aggregation of global cohort studies. Using genetic analysis to determine whether diabetes individuals are at the most risk for developing diabetic vascular complications (DVC) might lead to the development of more accurate early diagnostic biomarkers and the customization of care plans.ConclusionsA newer method that uses extensive evaluation of single nucleotide polymorphisms (SNP) in big datasets is Genome-Wide Association Studies (GWAS).