Non-invasive Biomarkers Research Articles

MiR-378a-3p and miR-181b-5p as nonalcoholic steatohepatitis non-invasive diagnostic biomarkers and their correlations with liver fibrosis

Nonalcoholic steatohepatitis (NASH) is one of the most common liver diseases that is diagnosed by biopsy and, therefore, requires the development of non-invasive tests for diagnosis. Serum levels of microRNAs were shown to correlate with the severity of various liver diseases, but the role of miR-378a and miR-181b-5p in NASH remains unclear. The current study aims to assess the serum expression level of miR-378a-3p and miR-181b-5p in patients with NASH and to find out the correlation of these indices with liver fibrosis. The case-control research was carried out on 60 patients with confirmed NASH relative to 50 healthy subjects. Extraction and reverse transcription of micro RNAs was performed using miRCURY LNA RT Kit (Qiagen, Maryland, USA) Detection of miR-378a-3p and miR-181b-5p was done using qPCR. It was shown that serum expression level of miR-378a-3p in NASH patients was downregulated with a median range fold change 0.29, while that of miR-181b-5p was upregulated with a median range fold change 13.08. The ROC curve was constructed to discriminate the NASH group from the healthy group. The optimal cut-off value of miR-378a-3p was ≤0.031 with a sensitivity of 65%, the optimal cut-off value of miR-181b-5p was ≥ 0.063 with a sensitivity of 93.3%. Statistically significant positive correlation between miR-181b-5p level and fibroscan data was demonstrated. The present study showed that serum miR-378a-3p and miR-181b-5p could be used as biomarkers of NASH. Keywords: liver fibrosis, miR-181b-5p, miR-378a-3p, nonalcoholic steatohepatitis (NASH)

Optimization and validation of echo times of point-resolved spectroscopy for cystathionine detection in gliomas

BackgroundCystathionine accumulates selectively in 1p/19q-codeleted gliomas, and can serve as a possible noninvasive biomarker. This study aims to optimize the echo time (TE) of point-resolved spectroscopy (PRESS) for cystathionine detection in gliomas, and evaluate the diagnostic accuracy of PRESS for 1p/19q-codeletion identification.MethodsThe TE of PRESS was optimized with numerical and phantom analysis to better resolve cystathionine from the overlapping aspartate multiplets. The optimized and 97 ms TE PRESS were then applied to 84 prospectively enrolled patients suspected of glioma or glioma recurrence to examine the influence of aspartate on cystathionine quantification by fitting the spectra with and without aspartate. The diagnostic performance of PRESS for 1p/19q-codeleted gliomas were assessed.ResultsThe TE of PRESS was optimized as (TE1, TE2) = (17 ms, 28 ms). The spectral pattern of cystathionine and aspartate were consistent between calculation and phantom. The mean concentrations of cystathionine in vivo fitting without aspartate were significantly higher than those fitting with full basis-set for 97 ms TE PRESS (1.97 ± 2.01 mM vs. 1.55 ± 1.95 mM, p < 0.01), but not significantly different for 45 ms method (0.801 ± 1.217 mM and 0.796 ± 1.217 mM, p = 0.494). The cystathionine concentrations of 45 ms approach was better correlated with those of edited MRS than 97 ms counterparts (r = 0.68 vs. 0.49, both p < 0.01). The sensitivity and specificity for discriminating 1p/19q-codeleted gliomas were 66.7% and 73.7% for 45 ms method, and 44.4% and 52.5% for 97 ms method, respectively.ConclusionThe 45 ms TE PRESS yields more precise cystathionine estimates than the 97 ms method, and is anticipated to facilitate noninvasive diagnosis of 1p/19q-codeleted gliomas, and treatment response monitoring in those patients. Medium diagnostic performance of PRESS for 1p/19q-codeleted gliomas were observed, and warrants further investigations.

Recently Emerging Liquid Biopsy-based Non-invasive Methylation Biomarkers for Gastric Cancer Diagnosis, Prognosis and Therapy Response Rrediction

Cancer, a burden upon the global population, has consistently maintained its prevalence throughout history. Gastric cancer compounds this burden and ranks as the fifth leading cause of cancer-related deaths worldwide. The clinical significance of early cancer diagnosis cannot be overstated. DNA methylation biomarkers, a rapidly growing field of liquid biopsy-based diagnostics, provide a minimal to non-invasive way to detect cancer at an early stage. In addition to diagnosis and prognosis, liquid biopsy is important in longitudinal monitoring and therapeutic response prediction, where traditional biopsy faces setbacks. Despite many challenges, DNA methylation being a stable and detectable molecular change attracts the scientific community to develop novel biomarkers and analytical methods to incorporate them into clinical practice. In this review, we have discussed non-invasive, DNA methylation-based gastric cancer biomarkers that have emerged recently. We have also addressed the prognosis and therapeutic response prediction associated with these biomarkers.

Noninvasive Disease Assessment in Eosinophilic Esophagitis With Fractionated Exhaled Nitric Oxide, Blood, and Fecal Biomarkers.

Eosinophilic Esophagitis (EoE) is a chronic inflammatory condition of the esophagus triggered by food and aeroallergens. There is a need for noninvasive biomarkers that reliably detect EoE in patients with cardinal symptoms and predict treatment response to reduce endoscopic evaluations. Nonasthmatic patients 18 years or above with suspected or diagnosed EoE, gastroesophageal reflux disease (GERD), and control individuals with indication for endoscopy were enrolled prospectively between November 2020 and May 2022. Participants underwent body plethysmography with fractionated exhaled nitric oxide (FeNO) level measurement. Besides, serum and fecal biomarkers were measured by ELISA. A follow-up examination was scheduled after treatment initiation in patients with active EoE. The median FeNO level in active EoE (20 ppb) was higher compared with GERD (15 ppb, P=0.038) and control individuals (14 ppb, P=0.046). Median FeNO did not significantly differ in EoE patients who underwent follow-up assessment after treatment response (20 ppb vs. 18 ppb, P=0.771). Serum EDN, ECP, and the absolute eosinophil blood count (AEC) were elevated in active EoE compared with control individuals but not compared with GERD except for AEC. Serum EDN, ECP and AEC decreased in EoE in remission at follow-up assessment. None of the fecal biomarkers was elevated in active EoE or during treatment. Assessment of FeNO may have diagnostic value in differentiating patients with active EoE from non-EoE patients but is not a suitable marker for monitoring disease activity. Serum EDN, ECP, TARC, and AEC levels are emerging as potential candidates for monitoring disease activity in EoE.

Gene expression analysis of microRNA-1285 in the South Indian oral squamous cell carcinoma population.

Oral squamous cell carcinoma (OSCC) is the most common oral malignant tumor, which has poor prognosis. The traditional investigative modality is invasive biopsy which is the gold standard for diagnosis. In recent years, alternative methods like non-invasive biomarkers have been studied for their potential role in early diagnosis and prognosis. Among them, microRNAs (miRNAs or miRs) are short non-coding RNAs that regulate gene expression in various diseases, including OSCC. Several miRNAs are being researched as non-invasive biomarkers as well as novel therapeutic targets in the treatment of OSCC. MiR expression can be upregulated or downregulated in OSCC. Among the reported miRNAs, miR-1285 is an important miRNA found to be involved in OSCC. The aim of the current study was to quantify the levels of miR-1285 in OSCC samples and to validate their potential role as biomarkers for OSCC detection. Sixteen samples of cancer tissue and normal tissue were evaluated from a total of 25 patients, in the study, conducted in the Department of Oral and Maxillofacial Surgery. The tissues were processed for H&E staining and gene expression analysis of miR-1285. The samples were collected after proper informed consent from the patients. Total RNA isolated was reverse transcribed into cDNA which was used in the gene expression analysis using qRT-PCR. The histopathological examination confirmed the OSCC cases and the gene expression analysis revealed that miR-1285 was significantly downregulated in OSCC tissues. Since miR-1285 showed significant difference between the OSCC and normal tissues it could be postulated as a potential biomarker and therapeutic target for OSCC. Further in-vitro and in-vivo studies could validate their functional role in OSCC.

Potential Diagnostic Role of Serum Fibroblast Growth Factor-19 in Hepatocellular Carcinoma.

A highly accurate diagnostic method is crucial to reduce mortality and increase hepatocellular carcinoma (HCC) survival. Current biomarkers have limited accuracy, and novel ones are needed. Fibroblast growth factor-19 (FGF-19) is overexpressed in HCC. This study aimed to assess FGF-19 as a potential novel diagnostic biomarker for HCC. This case-control study involved 114 individuals divided into three equal groups: HCC (n=38), Cirrhosis (n=38), and Control (n=38). HCC biomarkers included alpha-fetoprotein (AFP), Des-γ-carboxy prothrombin (DCP), and FGF-19. The three markers, FGF-19, DCP, and AFP, were significantly different between the three groups, except that DCP was comparable between HCC and Cirrhosis groups (p=1.000). All individuals in the control group had FGF-19 levels below the minimum level in the HCC group. Thus, FGF-19 had 100% sensitivity and specificity in differentiating HCC from healthy controls. FGF-19 can discriminate between HCC and Cirrhosis groups at a 140.8 pg/mL cutoff with sensitivity and specificity of 81.8% and 87.9%, respectively. The sensitivity of FGF-19 was higher than AFP, trending toward statistical significance (p=0.095). Combining FGF-19 with AFP, DCP, or both improved sensitivity but decreased specificity. FGF-19 is a possible noninvasive serum biomarker for HCC. Its combination with AFP or DCP improves the sensitivity for detecting HCC.

Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study

ObjectivesAlthough neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response...

OS02-08 Small RNA-sequencing based discovery of microRNAs for use as non-invasive biomarkers of CNS-injury

OS02-08 Small RNA-sequencing based discovery of microRNAs for use as non-invasive biomarkers of CNS-injury

Decreases in cT1 and liver fat content reflect treatment-induced histological improvements in MASH

MRI biomarkers of liver disease are robust and reproducible alternatives to liver biopsy. Emerging data suggest that absolute reduction in iron corrected T1 (cT1) of ≥ 80 ms and relative reduction in liver fat content of 30% reflect histological improvement. We aimed to validate the associations of changes to these noninvasive biomarkers with histological improvement, specifically the resolution of steatohepatitis. A retrospective analysis of participants from three interventional clinical trials who underwent multiparametric MRI to measure liver cT1 and liver fat content (LFC) (LiverMultiScan) alongside biopsies at baseline and end of study. Responders were defined as those achieving resolution of steatohepatitis with no worsening in fibrosis. Differences in the magnitude of change in cT1 and LFC between responders and non-responders was assessed. Individual patient data from 150 participants were included. There was a significant decrease in liver cT1 (-119 ms vs. -49 ms) and liver fat content (-65% vs. -29%) in responders compared to non-responders (P < .001) respectively. The diagnostic accuracy to identify responders was 0.72 (AUC) for both. The Youden's index for cT1 to separate responders from non-responders was -82 ms and for liver fat was a 58% relative reduction. Those achieving a ≥ 80 ms reduction in cT1 were 5-times more likely to achieve histological response (sens 0.68; spec 0.70). Those achieving a 30% relative reduction in liver fat were ∼4 times more likely to achieve a histological response (sens 0.77; spec 0.53). These results, from three combined drug trials, demonstrate that changes in multiparametric MRI markers of liver health (cT1 and PDFF) can predict histological response for steatohepatitis following therapeutic intervention. There is great interest in identifying suitable biomarkers that can be used to replace liver biopsy, or to identify those patients who would benefit from one, in both the clinical management of MASH and in drug development. We investigated the utility of two MRI-derived non-invasive tests, iron corrected T1 mapping (cT1) and liver fat content from proton density fat fraction (PDFF), to predict histological improvement in patients who had undergone experimental treatment for MASH. Using data from 150 people who participated in one of three clinical trials, we observed that a reduction in cT1 by over 80 ms and a relative reduction in PDFF of over 58% were the optimal thresholds for change that predicted resolution of steatohepatitis. PDFF as a marker of liver fat, and cT1 as a specific measure of liver disease activity, are both effective at identifying those who are likely responding to drug interventions and experiencing improvements in overall liver health. NCT02443116, NCT03976401, NCT03551522.

Imaging evaluation focused on microstructural tissue changes using tensor-valued diffusion encoding in breast cancers after neoadjuvant chemotherapy: is it a promising way forward?

Single diffusion encoding is a widely used, noninvasive technique for probing the tissue microstructure in breast tumors. However, it does not provide detailed information about the microenvironmental complexity. This study investigated the clinical utility of tensor-valued diffusion encoding for evaluating microstructural changes in breast cancer after neoadjuvant chemotherapy (NAC). We retrospectively included patients underwent chemotherapy for histologically proven invasive breast cancer between July 2020 and June 2023 and monitored the tumor response with breast magnetic resonance imaging (MRI), including tensor-valued diffusion encoding. We reviewed pre- and post-NAC MRIs regarding chemotherapy in 23 breast cancers. Q-space trajectory imaging (QTI) parameters were estimated at each time-point, and were compared with histopathological parameters. The mean total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), and microscopic fractional anisotropy (µFA) were significantly decreased on post-NAC MRI compared with pre-NAC MRI, with the large effect size (ES) in MKA and µFA (0.81±0.41 vs. 0.99±0.33, ES: 0.48, P=0.03; 0.48±0.30 vs. 0.73±0.27, ES: 0.88, P<0.001; 0.58±0.14 vs. 0.68±0.11, ES: 0.79, P=0.003; respectively). Regarding prognostic factors, tumors with high Ki-67 expression showed significantly lower pre-NAC mean diffusivity (MD) and higher pre-NAC µFA compared to tumors with low Ki-67 expression (0.98±0.09 vs. 1.25±0.20, P=0.002; and 0.72±0.07 vs. 0.57±0.10, P=0.005; respectively). And negative progesterone receptor (PR) group revealed significantly lower MKT, MKA, and isotropic mean kurtosis than positive PR group on the post-NAC MRI (0.60±0.31 vs. 1.03±0.40, P=0.008; 0.36±0.21 vs. 0.61±0.33, P=0.04; and 0.23±0.17 vs. 0.42±0.25, P=0.046; respectively). QTI parameters reflected the microstructural changes in breast cancer treated with NAC and can be used as noninvasive imaging biomarkers correlated with prognostic factors.

A novel tumor-derived exosomal gene signature predicts prognosis in patients with pancreatic cancer.

Pancreatic cancer is a devastating disease with poor prognosis. Accumulating evidence has shown that exosomes and their cargo have the potential to mediate the progression of pancreatic cancer and are promising non-invasive biomarkers for the early detection and prognosis of this malignancy. This study aimed to construct a gene signature from tumor-derived exosomes with high prognostic capacity for pancreatic cancer using bioinformatics analysis. Gene expression data of solid pancreatic cancer tumors and blood-derived exosome tissues were downloaded from The Cancer Genome Atlas (TCGA) and ExoRBase 2.0. Overlapping differentially expressed genes (DEGs) in the two datasets were analyzed, followed by functional enrichment analysis, protein-protein interaction networks, and weighted gene co-expression network analysis (WGCNA). Using the least absolute shrinkage and selection operator (LASSO) regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was constructed based on the TCGA dataset, which was validated by an external validation dataset, GSE62452. The prognostic power of this gene signature and its relationship with various pathways and immune cell infiltration were analyzed. A total of 166 overlapping DEGs were identified from the two datasets, which were markedly enriched in functions and pathways associated with the cell cycle. Two key modules and corresponding 70 exosomal DEGs were identified using WGCNA. Using LASSO Cox regression of prognosis-related exosomal DEGs, a tumor-derived exosomal gene signature was built using six exosomal DEGs (ARNTL2, FHL2, KRT19, MMP1, CDCA5, and KIF11), which showed high predictive performance for prognosis in both the training and validation datasets. In addition, this prognostic signature is associated with the differential activation of several pathways, such as the cell cycle, and the infiltration of some immune cells, such as Tregs and CD8+ T cells. This study established a six-exosome gene signature that can accurately predict the prognosis of pancreatic cancer.

Gender-specific microbial signatures in saliva: Unveiling the association between the oral microbiome and the pathogenesis of glioma

The intricate interplay between the human oral microbiome and systemic health is increasingly being recognized, particularly in the context of central nervous system pathologies such as glioblastoma. In this study, we aimed to elucidate gender-specific differences in the salivary microbiome of glioma patients by utilizing 16S rRNA sequencing data from publicly available salivary microbiome datasets. We conducted comprehensive bioinformatics analysis, encompassing quality control, noise reduction, species classification, and microbial community composition analysis at various taxonomic levels. Machine learning algorithms were employed to identify microbial signatures associated with glioma. When compared to healthy controls, our analysis revealed distinct differences in the salivary microbiota of glioma patients. Notably, the genera Leptotrichia and Atopobium exhibited significant variations in abundance between genders. Leptotrichia was prevalent in healthy females but exhibited a reduced abundance in female glioma patients. In contrast, Atopobium was more abundant in male glioma patients. These findings suggest that hormonal influences might play a role in shaping the salivary microbiome and its association with glioma. We utilized a combination of LASSO-logistic regression and random forest models for feature selection, and identified key microbial features that differentiated glioma patients from healthy controls. We developed a diagnostic model with high predictive accuracy and area under the curve and principal component analysis metrics confirmed its robustness. The analysis of microbial markers, including Atopobium and Leptotrichia, highlighted the potential of the salivary microbiota as a non-invasive biomarker for the diagnosis and prognosis of glioma. Our findings highlight significant gender-specific disparities in the salivary microbiome of patients with glioma, offering new insights into the pathogenesis of glioma and paving the way for innovative diagnostic and therapeutic strategies. The use of saliva as a diagnostic fluid, given its ease of collection and non-invasive nature, holds immense promise for monitoring systemic health and the trajectory of disease. Future research should focus on investigating the underlying mechanisms by which the salivary microbiome influences the development of glioma and identifying potential microbiome-targeted therapies to enhance the management of glioma.

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.

Circulating miRNA panels as a novel non-invasive diagnostic, prognostic, and potential predictive biomarkers in non-small cell lung cancer (NSCLC)

BackgroundNon-small cell lung cancer (NSCLC) is characterised by its aggressiveness and poor prognosis. Early detection and accurate prediction of therapeutic responses remain critical for improving patient outcomes. In the present study, we investigated the potential of circulating microRNA (miRNA) as non-invasive biomarkers in patients with NSCLC.MethodsWe quantified miRNA expression in plasma from 122 participants (78 NSCLC; 44 healthy controls). Bioinformatic tools were employed to identify miRNA panels for accurate NSCLC diagnosis. Validation was performed using an independent publicly available dataset of more than 4000 NSCLC patients. Next, we correlated miRNA expression with clinicopathological information to identify independent prognostic miRNAs and those predictive of anti-PD-1 treatment response.ResultsWe identified miRNA panels for lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) diagnosis. The LUAD panel consists of seven circulating miRNAs (miR-9-3p, miR-96-5p, miR-147b-3p, miR-196a-5p, miR-708-3p, miR-708-5p, miR-4652-5p), while the LUSC panel comprises nine miRNAs (miR-130b-3p, miR-269-3p, miR-301a-5p, miR-301b-5p, miR-744-3p, miR-760, miR-767-5p, miR-4652-5p, miR-6499-3p). Additionally, miR-135b-5p, miR-196a-5p, miR-31-5p (LUAD), and miR-205 (LUSC) serve as independent prognostic markers for survival. Furthermore, two miRNA clusters, namely miR-183/96/182 and miR-767/105, exhibit predictive potential in anti-PD-1-treated LUAD patients.ConclusionsCirculating miRNA signatures demonstrate diagnostic and prognostic value for NSCLC and may guide treatment decisions in clinical practice.

Challenges around diagnosis of early onset colorectal cancer, and the case for screening.

Colorectal cancer (CRC) is the third most diagnosed cancer in the world, with an estimated 1.93 million cases diagnosed in 2020. While the overall CRC incidence in many countries is falling there has been a dramatic increase in CRC in those aged under 50 (early onset colorectal cancer, EOCRC). The reason for this increase in EOCRC is unknown. As the best predictor of survival is stage at diagnosis, early diagnosis is likely to be beneficial and population screening may facilitate this. A narrative review of the literature was undertaken. Improving time to diagnosis in symptomatic patients is beneficial. However, by the time symptoms develop, over a third of patients already have metastatic disease. Screening asymptomatic patients (with Faecal Immunochemical test (FIT) and colonoscopy) has been proved to be effective in older patients (>60 years). In younger populations, the decreasing incidence rates of CRC previously made cost effectiveness, compliance and therefore benefit questionable. Now, with the increasing incidence of CRC in those under 50 years of age, modelling suggests screening with FIT and colonoscopy is cost effective from 40 years of age. There is evidence that some countries screening below 50 have prevented the rise in EOCRC incidence. Additionally the use of new and novel non-invasive biomarkers may also be able to improve the accuracy of screening asymptomatic patients. Diagnosis of EOCRC once symptoms develop is often too late, and screening patients from age 40 is the best way to improve outcomes in this group.

Super-Enhancer Driven LIF/LIFR-STAT3-SOX2 Regulatory Feedback Loop Promotes Cancer Stemness in Head and Neck Squamous Cell Carcinoma.

Super-enhancers (SEs) have been recognized as key epigenetic regulators underlying cancer stemness and malignant traits by aberrant transcriptional control and promising therapeutic targets against human cancers. However, the SE landscape and their roles during head and neck squamous cell carcinoma (HNSCC) development especially in cancer stem cells (CSCs) maintenance remain underexplored yet. Here, we identify leukemia inhibitory factor (LIF)-SE as a representative oncogenic SE to activate LIF transcription in HNSCC. LIF secreted from cancer cells and cancer-associated fibroblasts promotes cancer stemness by driving SOX2 transcription in an autocrine/paracrine manner, respectively. Mechanistically, enhancer elements E1, 2, 4 within LIF-SE recruit SOX2/SMAD3/BRD4/EP300 to facilitate LIF transcription; LIF activates downstream LIFR-STAT3 signaling to drive SOX2 transcription, thus forming a previously unknown regulatory feedback loop (LIF-SE-LIF/LIFR-STAT3-SOX2) to maintain LIF overexpression and CSCs stemness. Clinically, increased LIF abundance in clinical samples correlate with malignant clinicopathological features and patient prognosis; higher LIF concentrations in presurgical plasma dramatically diminish following cancer eradication. Therapeutically, pharmacological targeting LIF-SE-LIF/LIFR-STAT3 significantly impairs tumor growth and reduces CSC subpopulations in xenograft and PDX models. Our findings reveal a hitherto uncharacterized LIF-SE-mediated auto-regulatory loop in regulating HNSCC stemness and highlight LIF as a novel noninvasive biomarker and potential therapeutic target for HNSCC.

Using Urine Biomarkers to Differentiate Bladder Dysfunctions in Women with Sensory Bladder Disorders.

Sensory bladder disorders encompass several distinct conditions with overlapping symptoms, which pose diagnostic challenges. This study aimed to evaluate urine biomarkers for differentiating between various sensory bladder disorders, including non-Hunner's interstitial cystitis (NHIC), detrusor overactivity (DO), hypersensitive bladder (HSB), and urodynamically normal women. A retrospective analysis of 191 women who underwent a videourodynamic study (VUDS) was conducted, with some also receiving cystoscopic hydrodistention to confirm the presence of NHIC. Participants were categorized into four groups: DO (n = 51), HSB (n = 29), NHIC (n = 81), and normal controls (n = 30). The urine levels of inflammatory and oxidative stress biomarkers were measured. The DO patients exhibited elevated IP-10 levels, while the HSB patients had decreased TAC and 8-OHdG levels. The NHIC patients showed lower IL-2 and higher TNF-α levels. A TNF-α ≥ 1.05 effectively identified NHIC, with an AUROC of 0.889, a sensitivity of 98.8%, and a specificity of 81.3%. An IP-10 ≥ 6.31 differentiated DO with an AUROC of 0.695, a sensitivity of 56.8%, and a specificity of 72.3%. An 8-OHdG ≤ 14.705 and a TAC ≤ 528.7 identified HSB with AUROCs of 0.754 and 0.844, respectively. The combination of 8-OHdG and TAC provided an AUROC of 0.853 for HSB. These findings suggest that TNF-α, IP-10, TAC, 8-OHdG, and IL-2 are promising non-invasive biomarkers for distinguishing between these conditions, which may improve diagnosis and management.

Protein Biomarkers of Gastric Preneoplasia and Cancer Lesions in Blood: A Comprehensive Review.

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. It is often associated with a bad prognosis because of its asymptomatic phenotype until advanced stages, highlighting the need for its prevention and early detection. GC development is preceded by the emergence of gastric preneoplasia lesions (GPNLs), namely atrophic gastritis (AG), intestinal metaplasia (IM), and dysplasia (DYS). GC is currently diagnosed by endoscopy, which is invasive and costly and has limited effectiveness for the detection of GPNLs. Therefore, the discovery of non-invasive biomarkers in liquid biopsies, such as blood samples, in order to identify the presence of gastric preneoplasia and/or cancer lesions at asymptomatic stages is of paramount interest. This comprehensive review provides an overview of recently identified plasma/serum proteins and their diagnostic performance for the prediction of GPNLs and early cancer lesions. Autoantibodies appear to be promising biomarkers for AG, IM and early gastric cancer detection, along with inflammation and immunity-related proteins and antibodies against H. pylori virulence factors. There is a lack of specific protein biomarkers with which to detect DYS. Despite the need for further investigation and validation, some emerging candidates could pave the way for the development of reliable, non-invasive diagnostic tests for the detection and prevention of GC.

Identification of autoantibodies as potential non-invasive biomarkers for intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19–9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

Hair mercury isotopes, a noninvasive biomarker for dietary methylmercury exposure and biological uptake.

Background. Fish and rice are the main dietary sources of methylmercury (MeHg); however, rice does not contain the same beneficial nutrients as fish, and these differences can impact the observed health effects of MeHg. Hence, it is important to validate a biomarker, which can distinguish among dietary MeHg sources. Methods. Mercury (Hg) stable isotopes were analyzed in hair samples from peripartum mothers in China (n = 265). Associations between mass dependent fractionation (MDF) (δ202Hg) and mass independent fractionation (MIF) (Δ199Hg) (dependent variables) and dietary MeHg intake (independent variable) were investigated using multivariable regression models. Results. In adjusted models, hair Δ199Hg was positively correlated with serum omega-3 fatty acids (a biomarker for fish consumption) and negatively correlated with maternal rice MeHg intake, indicating MIF recorded in hair can be used to distinguish MeHg intake predominantly from fish versus rice. Conversely, in adjusted models, hair δ202Hg was not correlated with measures of dietary measures of MeHg intake. Instead, hair δ202Hg was strongly, negatively correlated with hair Hg, which explained 27-29% of the variability in hair δ202Hg. Conclusions. Our results indicated that hair Δ199Hg can be used to distinguish MeHg intake from fish versus rice. Results also suggested that lighter isotopes were preferentially accumulated in hair, potentially reflecting Hg binding to thiols (i.e., cysteine); however, more research is needed to elucidate this hypothesis. Broader impacts include 1) validation of a non-invasive biomarker to distinguish MeHg intake from rice versus fish, and 2) the potential to use Hg isotopes to investigate Hg binding in tissues.