Biomarker For Carcinoma Research Articles

Construction of a prognostic model based on disulfidptosis-related genes and identification of CCNA2 as a novel biomarker for hepatocellular carcinoma

BackgroundDisulfidptosis, identified as an innovative form of cellular death subsequent to cuproptosis, is currently under investigation for its mechanisms in oncological contexts. In-depth analyses exploring the relationship between disulfidptosis-related genes (DRGs) and hepatocellular carcinoma (HCC) are currently limited.MethodsTranscriptomic data and clinical information were retrieved from the TCGA and GEO databases (GSE76427 and GSE54236), concentrating on the expression levels of 24 DRGs. Subsequently, multifactor and LASSO regression analyses were utilized to construct the 5-DRG prognostic signature. Immunohistochemistry (IHC) was employed to assess Cyclin A2 (CCNA2) protein expression levels. Quantitative real-time PCR (qRT-PCR) and western blot analyses were conducted to detect transcriptomic and protein expression of CCNA2-targeting short interfering RNA (siRNA). The Cell Counting Kit-8 (CCK-8) assay, EdU staining, and scratch experiments were employed to observe the proliferation and migration of hepatoma cell lines subsequent to CCNA2 inhibition.ResultsThree HCC patterns were identified, among which pattern B exhibited the the most unfavorable survival outcomes. Five DRGs (STC2, PBK, CCNA2, SERPINE1, and SLC6A1) were involved to establish the 5-DRG prognostic signature. High-risk groups (HRGs) exhibited prolonged survival durations in comparison to low-risk groups (LRGs). Both bioinformatics analyses and experimental methodologies corroborated the association of CCNA2 with poor prognosis in HCC patients. Functional studies elucidated that interference with CCNA2 significantly inhibited proliferation and migration, while simultaneously promoting apoptosis in hepatoma cells and resulting in the downregulation of epithelial-mesenchymal transition (EMT)-related protein markers.ConclusionsThe 5-DRG prognostic signature is proficient in predicting clinical outcomes, informing therapeutic strategies, and elucidating the characteristics of the immune microenvironment in HCC patients. Furthermore, this study elucidates the potential of CCNA2 as an innovative biomarker for HCC.

Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.

Mitochondrial-related drug resistance lncRNAs as prognostic biomarkers in laryngeal squamous cell carcinoma

Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck that significantly impacts patients' quality of life, with chemotherapy resistance notably affecting prognosis. This study aims to identify prognostic biomarkers to optimize treatment strategies for LSCC. Using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), combined with mitochondrial gene database analysis, we identified mitochondrial lncRNAs associated with drug resistance genes. Key long non-coding RNAs (lncRNAs) were selected through univariate Cox regression and Lasso regression, and a multivariate Cox regression model was constructed to predict prognosis. We further analyzed the differences in immune function and biological pathway enrichment between high- and low-risk groups, developed a nomogram, and compared drug sensitivity. Results showed that the prognostic model based on seven mitochondrial lncRNAs could serve as an independent prognostic factor, with Area Under the Curve (AUC) values of 0.746, 0.827, and 0.771 at 1, 3, and 5 years, respectively, outperforming some existing models, demonstrating high predictive performance. Significant differences were observed in immune function and drug sensitivity between the high- and low-risk groups. The risk prediction model incorporating seven drug resistance-related mitochondrial lncRNAs can accurately and independently predict the prognosis of LSCC patients.

A Critical Review on microRNAs as Prognostic Biomarkers in Laryngeal Carcinoma

During the past decade, a vast number of studies were dedicated to unravelling the obscurities of non-coding RNAs in all fields of the medical sciences. A great amount of data has been accumulated, and consequently a natural need for organization and classification in all subfields arises. The aim of this review is to summarize all reports on microRNAs that were delineated as prognostic biomarkers in laryngeal carcinoma. Additionally, we attempt to allocate and organize these molecules according to their association with key pathways and oncogenes affected in laryngeal carcinoma. Finally, we critically analyze the common shortcomings and biases of the methodologies in some of the published papers in this area of research. A literature search was performed using the PubMed and MEDLINE databases with the keywords “laryngeal carcinoma” OR “laryngeal cancer” AND “microRNA” OR “miRNA” AND “prognostic marker” OR “prognosis”. Only research articles written in English were included, without any specific restrictions on study type. We have found 43 articles that report 39 microRNAs with prognostic value associated with laryngeal carcinoma, and all of them are summarized along with the major characteristics and methodology of the respective studies. A second layer of the review is structural analysis of the outlined microRNAs and their association with oncogenes and pathways connected with the cell cycle (p53, CCND1, CDKN2A/p16, E2F1), RTK/RAS/PI3K cascades (EGFR, PI3K, PTEN), cell differentiation (NOTCH, p63, FAT1), and cell death (FADD, TRAF3). Finally, we critically review common shortcomings in the methodology of the papers and their possible effect on their results.

Novel Multimode Assay Based on Asymmetrically Competitive CRISPR and Raman Barcode Spectra for Multiple Hepatocellular Carcinoma Biomarkers Detection.

Commercial pregnancy test strips (PTS) possess the advantages of lower price, higher stability, and better repeatability and have been popularized to integrate with novel sensing strategies to detect other disease biomarkers, which accelerates the commercialization process of those novel sensing strategies. However, the current integration of novel sensing strategies into commercial PTS still faced the problems of insufficient quantification, low sensitivity, and lack of multiple detection capabilities. Hence, we proposed the concept of "visual classification recognition, spectral signal subdivision" for multiple hepatocellular carcinoma biomarkers (miRNA122 and miRNA233) detection with dual signals based on asymmetric competitive CRISPR (acCRISPR) and surface-enhanced Raman spectroscopy coupling with PTS, named the acCRISPR-PTS-SERS assay. In this assay, acCRISPR was used as a nonamplified cascaded signal amplification method to improve the sensitivity of detection. Two AuNPs-based core-shell Raman tags, each corresponding to different miRNA biomarkers, were used to achieve both visual recognition and spectral segmentation to enhance the quantification of PTS detection and the capability for multiple detection. Under the optimal conditions, the LOD for miRNA122 and miRNA223 were 10.36 and 4.65 fM, respectively. The sensitivity was enhanced by nearly 2 orders of magnitude. In the future, simultaneous hand-held detection for fingerprint barcodes of different cancers can be achieved with the assistance of a microfluidic chip and smartphone.

Engrailed-2 (EN2) protein in cervical mucus: a novel biomarker for endometrial carcinoma.

This study aims to demonstrate that the EN2 protein in cervical mucus may serve as a novel biomarker for screening endometrial cancer. This study included 133 patients who were treated at Beijing Obstetrics and Gynecology Hospital. According to the pathological results of hysteroscopy endometrial biopsy, the patients were divided into endometrial cancer group (n = 55), endometrial atypical hyperplasia group (n = 16), benign lesion group (n = 28), and control group (n = 34). All patients collected cervical mucus before hysteroscopy, and the level of EN2 protein in cervical mucus was detected by ELISA in the four groups of patients. Nine patients who underwent surgical treatment for endometrial cancer were included, and endometrial cancer lesions and adjacent tissues without endometrial cancer infiltration were taken from the endometrial cancer lesions and cervical tissues. Fifteen patients who underwent hysterectomy for benign lesions were included, and endometrial and cervical tissues were taken from the endometrial cancer lesions and adjacent tissues without endometrial cancer infiltration. PT-PCR, immunohistochemistry, and WB were used to verify the expression differences of EN2 protein in cancerous lesions and endometrial tissues without endometrial infiltration, cervical tissues, and endometrial tissues without endometrial lesions. In addition, HEC1B, KLE, and HeLa cell lines were used to characterize EN2 overexpression in endometrial cancer cells. Immunohistochemistry, RT-PCR, and confocal analysis were used to detect the expression of EN2 protein in different cell lines. In different groups, the average concentration of EN2 protein in cervical mucus in the EC group was significantly higher than that in the benign group and the normal control group (573.9 ± 123.4ng/mL vs 153.5 ± 106.2ng/mL and 153.0 ± 107.5ng/mL, P < 0.001). The concentration of EN2 protein in EIN3 case specimens was significantly higher than that in the normal control group (P < 0.001). ROC analysis showed that the optimal threshold for diagnosing endometrial cancer in cervical mucus was 321.1ng/ml, with a sensitivity of 92.6% and specificity of 95.4% for distinguishing EINIII, EC, and non-cancerous cases. In clinical specimens, the expression level of EN2 protein in endometrial cancer tissues was significantly higher than that in endometrial tissues and cervical tissues without endometrial cancer infiltration. In addition, PT-PCR, immunohistochemistry, suggest that EN2 protein is overexpressed in endometrial cancer cell lines compared to cervical adenocarcinoma cells (P < 0.01). The concentration of EN2 protein in cervical mucus can effectively identify endometrial cancer and precancerous lesions. The increased expression of EN2 protein in endometrial cancer lesions leads to an increase in the concentration of EN2 protein in the cervical mucus of endometrial cancer patients.This is a small single-center study, and larger studies are needed to determine the role of EN2 protein in the diagnosis of endometrial cancer.

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BackgroundThe integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.ResultsWe propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.ConclusionsExperimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

LncRNAs SOX2-OT and NEAT1 act as a potential biomarker for esophageal squamous cell carcinoma

Despite strides in diagnostic and therapeutic approaches for ESCC, patient survival rates remain relatively low. Recent studies highlight the pivotal role of long non-coding RNAs (lncRNAs) in regulating diverse cellular activities in humans. Dysregulated lncRNAs have emerged as potential diagnostic indicators across various cancers, including ESCC. However, further research is necessary to effectively leverage ESCC-associated lncRNAs in clinical settings. Understanding their clinical significance for ESCC diagnosis and their mechanisms can pave the way for more effective therapeutic strategies. Our qRT-PCR analysis revealed significant downregulation of SOX2-OT (~ 2.02-fold) and NEAT1 (~ 1.53-fold) in ESCC blood samples. These lncRNAs show potential as biomarkers for distinguishing ESCC patients from healthy individuals, with ROC curves and AUC values of 0.736 for SOX2-OT and 0.621 for NEAT1. Further analysis examined the correlation between SOX2-OT and NEAT1 expression and various clinicopathological factors, including age, gender, smoking, alcohol use, hot beverage intake, tumor grade, and TNM stages. In-silico studies highlighted their roles in miRNA sponging via mTOR and MAPK pathways, while co-expression network analysis identified associated genes. This research paves the way for future studies on ESCC prognosis using SOX2-OT and NEAT1 as predictive markers. By thoroughly investigating the functions of these lncRNAs, we aim to deepen our understanding of their potential as diagnostic markers and their role in facilitating effective therapeutic interventions for esophageal squamous cell carcinoma (ESCC) within clinical contexts.

Tissue factor pathway inhibitor 2 (TFPI2) is a potential serum biomarker for clear cell renal carcinoma.

Renal and ovarian clear cell carcinoma (CCC) are both characterized by a clear cytoplasm and exhibit similar genomic alterations and clinical characteristics. We hypothesized that both CCCs may share clinical biomarker. Tissue factor pathway inhibitor 2 (TFPI2), a serine protease inhibitor, has emerged as a promising serum biomarker for ovarian CCC, and we evaluated the efficacy of TFPI2 as a biomarker for renal cell carcinoma (RCC). Serum samples were collected from patients with RCC and healthy volunteers, and TFPI2 levels were measured. Expression of TFPI2 in each cell type was evaluated using single-cell RNA sequencing. Survival analyses according to TFPI2 expression levels were performed based on publicly available databases. Serum TFPI2 was significantly elevated in patients with RCC compared to healthy volunteers, particularly those with clear cell histology. Metastatic RCC tumors exhibited higher TFPI2 than localized RCCs. Moreover, higher TFPI2 correlated with higher Fuhrman grades in clear cell RCC. Publicly available databases showed an association between TFPI2 expression and overall survival, particularly in clear cell RCC. Single-cell RNA sequencing confirmed TFPI2 expression in clear cell RCC and normal kidney tubular epithelial cells. TFPI2 has emerged as a potential serum biomarker for RCC, offering avenues for improved detection and prognostication.

ATP6V1C1, associated with the tumor microenvironment and mTORC1 signaling pathway, is a potential diagnostic, prognostic, and therapeutic biomarker for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a global health challenge with high mortality. ATP6V1C1, one of the subunit genes of vacuolar adenosine triphosphatase (V-ATPase), is a potential oncogene. However, its role in HCC remains unclear.Materials and methodsDifferential analysis of mRNA and microRNA (miRNA), combined with machine learning, identified ATP6V1C1 as a potential biomarker for HCC. The expression and prognostic role of ATP6V1C1 in HCC were evaluated. Additionally, we explored the distribution of ATP6V1C1 in HCC tumor microenvironment (TME) at single-cell and spatial transcriptome levels. Furthermore, the association between ATP6V1C1 and malignant biological features, TME characteristics, and therapy response in HCC was investigated. Finally, in vitro experiments validated the effects of ATP6V1C1 on the malignant phenotype of HCC.ResultsATP6V1C1 had higher expression in HCC tissues compared to paired normal tissues. Upregulated ATP6V1C1 was associated with poor HCC prognosis. ATP6V1C1 was primarily expressed in malignant cells and the tumor region in HCC TME. A positive correlation was observed between ATP6V1C1 expression and the activation of cancer-related pathways. The high ATP6V1C1 expression group exhibited increased pro-tumorigenic immune infiltration, inhibited anti-tumor immune activity, and high tumor proliferation rate. HCC patients of low ATP6V1C1 expression group had more clinical response to anti-tumor therapies. Knockdown of ATP6V1C1 impaired the proliferation, migration, and invasion of HCC cells by downregulating the mTORC1 signaling pathway.ConclusionATP6V1C1 multifacetedly contributes to the oncogenesis and progression of HCC and is a promising diagnostic and prognostic biomarker with predictive value on therapy response.

Bioinformatics combined with network pharmacology and experimental validation to identify key biomarkers of hepatocellular carcinoma and corresponding compounds in Radix Astragali and Pueraria Mirifica.

The occurrence and death rates of primary hepatocellular carcinoma (HCC) are increasing, and there remains a shortage of effective oral medications with minimal side effects. We aim to identify potential biomarkers and compounds from Radix Astragali (RA) and Pueraria Mirifica (PM) to treat liver cancer and improve prognosis. Differentially expressed genes (DEGs) associated with HCC were identified by bioinformatics analysis of three datasets, GSE112791, GSE101685, and GSE45114. Using public databases to predict the bioactive components and possible targets of RA and PM. Target crossover from Gene Expression Omnibus (GEO) and public databases were used to identify potential biomarkers for HCC. Subsequently, validation and prognostic value analyses were performed using the Gene Expression Profile Interaction Analysis (GEPIA) platform. The Cytoscape software created a network of "compound targets" to pinpoint compounds linked to the biomarkers. Molecular docking techniques were utilized to validate the connection between these compounds and the identified biomarkers. Ultimately, the HepG2 liver cancer cell line was chosen to assess the inhibitory effect of Hederagenin (HDG) and to confirm the expression of ADH1B through Western blot analysis. In this study, four key biomarkers (NR1I2, ADH1B, NQO1, GHR) were identified. Molecular docking showed that these four core targets could form stable conformations with the corresponding compounds. As the drug concentration decreases, the inhibitory effect on HepG2 diminishes, and the survival rate of HepG2 cells significantly declines following the administration of 100 µmol/L HDG. Compared to the control, the expression of ADH1B protein is significantly increased in HepG2 cells treated with 100 µmol/L HDG. The study identified four key biomarkers (ADH1B, GHR, NQO1, NR1I2) that have prognostic ability for HCC. This study provides biomarkers and potential targeted monomeric medicines for treating HCC.

Development of a prognostic signature for overall survival using peripheral blood biomarkers in head and neck squamous cell carcinoma treated with immune checkpoint inhibitors

BackgroundWe previously reported in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) treated with immune checkpoint inhibitors (ICIs), pretreatment higher lactate dehydrogenase (LDH) and absolute (abx) neutrophils as well as lower percent (%) lymphocytes correlated with worse overall survival (OS). In this study we aimed to develop a prognostic signature for HNSCC treated with ICIs using these peripheral blood biomarkers (PBBMs).MethodsAdults with R/M HNSCC treated with ICIs at our institution from 08/2012 to 03/2021 with pretreatment PBBMs were included. Follow-up continued until 02/15/2022. The cohort (n = 151) was randomly split into training (n = 100) and testing (n = 51) datasets. A prognostic score incorporating LDH, % lymphocytes, and abx neutrophils was developed from the training dataset using Cox proportional hazards regression. In the training dataset, a grid search identified the optimal cutpoints classifying patients into high, medium, and low-risk groups (trichotomized signature) as well as high vs. low-risk groups (dichotomized signature). The prognostic score, dichotomized and trichotomized signatures were then validated in the testing dataset.ResultsTraining and testing datasets showed no clinically meaningful differences in clinicodemographic characteristics or PBBMs. An OS prognostic model was developed from the training dataset: Risk score = 1.24*log10(LDH) − 1.95*log10(% lymphocytes) + 0.47*log10(abx neutrophils). Optimal risk score cutpoints for the dichotomized and trichotomized signatures were defined in the training dataset, and Kaplan-Meier curves for both dichotomized and trichotomized signatures showed good separation between risk groups. Risk scores were calculated in the testing dataset, where the trichotomized signature demonstrated overlap between low and medium-risk groups but good separation from the high-risk group while the dichotomized signature showed clear separation between low and high-risk groups. Higher risk score correlated with worse OS (HR 2.08, [95%CI 1.17–3.68], p = 0.012). Progression-free survival Kaplan-Meier curves likewise showed excellent separation between dichotomized risk groups in the training and testing datasets.ConclusionsWe developed a prognostic signature for OS based on 3 previously identified PBBMs for HNSCC treated with ICIs and identified a high-risk group of patients least likely to have survival benefit from ICIs. This signature may improve ICI patient selection and warrants validation in an independent cohort as well as correlation with CPS.

Mac-2 binding protein glycosylation isomer as a potential biomarker of hepatocellular carcinoma in HCV-cured patients

ObjectiveMac-2 binding protein glycosylation isomer (M2BPGi) is produced in the extracellular matrix and serves as an indicator of hepatic stellate cell activation. Assessing M2BPGi levels could aid in predicting hepatocellular carcinoma (HCC) in individuals with hepatitis C virus (HCV). The objective of this study was to evaluate the usefulness of M2BPGi as a biomarker for HCC in HCV patients and its association with disease severity and progression.MethodsThis study included patients who were cured of chronic hepatitis C virus. The patients were divided into three subgroups: HCV without cirrhosis, HCV with cirrhosis, and HCV with HCC. These subgroups were then compared to a subgroup of healthy volunteers. In addition to routine laboratory investigations, M2BPGi levels were measured in all the enrolled subjects.ResultsThe level of serum M2BPGi was significantly greater in the HCV with cirrhosis and HCC groups than in the control group (P < 0.001). Additionally, it was significantly greater in multifocal HCC than in those with unifocal HCC (P < 0.001), and it was directly proportional to the size of the focal lesion of HCC (P = 0.001). The cutoff for serum M2BPGi in diagnosing HCC was ≥ 0.869 (C.O.I), with an AUC of 0.762, a sensitivity of 78.6%, and a specificity of 61.9% (P = 0.004). Furthermore, the cutoff for predicting multifocality was > 0.93 (C.O.I), with an AUC of 0.73, sensitivity of 66.7%, and specificity of 63.8% (P = 0.03). Although the AFP level was still superior in predicting cirrhosis and HCC, the M2BPGi level was better at predicting the size and diagnostic value of HCC when the AFP level was normal. The cutoff for M2BPGi in this case was 0.903(C.O.I), with a sensitivity of 80%, specificity of 75%, and an accuracy of 76.25%. M2BPGi was independently associated with the CRP level (β = 0.484, P = 0.001) and the size of the HCC focal lesion (β = 1.422, P = 0.001).ConclusionM2BPGi can be used as an effective marker to assess the biological behavior and aggressiveness of HCC. Further studies are warranted on a large scale of patients to confirm our findings.

Acid Sphingomyelinase Activation and ROS Generation Potentiate Antiproliferative Effects of Mitomycin in HCC.

Sphingolipids play a major role in the regulation of hepatocellular apoptosis and proliferation. We have previously identified sphingolipid metabolites as biomarkers of chronic liver disease and hepatocellular carcinoma. Human hepatocellular carcinoma cell lines were transfected with a plasmid vector encoding for acid sphingomyelinase. Overexpressing cells were subsequently treated with mitomycin and cell proliferation, acid sphingomyelinase activity, sphingolipid concentrations, and generation of reactive oxygen species were assessed. The stimulation of acid sphingomyelinase-overexpressing cell lines with mitomycin showed a significant activation of the enzyme (p < 0.001) followed by an accumulation of various ceramide species (p < 0.001) and reactive oxygen radicals (p < 0.001) as compared to control transfected cells. Consequently, a significant reduction in cell proliferation was observed in acid sphingomyelinase-overexpressing cells (p < 0.05) which could be diminished by the simultaneous application of antioxidant agents. Moreover, the application of mitomycin induced significant alterations in mRNA expression levels of ceramidases and sphingosine kinases (p < 0.05). Our data suggest that the overexpression of the acid sphingomyelinase in human hepatoma cell lines enhances the in vitro antiproliferative potential of mitomycin via accumulation of ceramide and reactive oxygen species. The selective activation of acid sphingomyelinase might offer a novel therapeutic approach in the treatment of hepatocellular carcinoma.

Genes of DLK1-DIO3 Locus and miR-379/656 Cluster is a Potential Diagnostic and Prognostic Marker in Patients With Hepatocellular Carcinoma: A Systems Biology Study

BackgroundHepatocellular carcinoma is the sixth most common malignancy reported globally. This highlights the need for reliable biomarkers that can be employed for diagnostic and prognostic applications. The present study aimed to classify and characterize the clinical potential of DLK1-DIO3 and miR-379/656 cluster genes in hepatocellular carcinoma. MethodsWe extensively studied the clinical potential of DLK1-DIO3 genes through a comprehensive systems biology approach and assessed the diagnostic and prognostic potential of the genes associated with the region. Additionally, we have predicted the gene targets of the miR-379/656 cluster associated with the locus and have identified the gene ontology, pathway, and disease associations. ResultsWe report this region as a potential biomarker for hepatocellular carcinoma. About thirty clustered miRNAs, a long-non-coding RNA, and two coding genes of the region were underexpressed in tumors. ROC analysis identified 11 clustered miRNAs with diagnostic potential. Survival analyses identified MEG3 and the miR-379/656 cluster as prognostically significant. Further, the Random-Forest model predicted that the miRNA cluster classifies patients according to TNM staging. Furthermore, overrepresentation analysis identified several key pathways, molecular functions, and biological processes associated with the cluster gene targets. ConclusionOur study suggests that DLK1-DIO3 genes, miR-379/656 cluster, and its target gene network might be potential diagnostic and prognostic markers for HCC management and therapy.

Cytokines levels in saliva in periodontitis, oral potentially malignant disorders and squamous cell carcinoma

Biopsy and histopathological examination continue to be the gold standards for diagnosing oral cancer and oral potentially malignant disorders (OPMD). The prognosis of these conditions is significantly influenced by established factors such as the degree of dysplasia, tumour size, grade, and patient age. While these traditional diagnostic approaches are crucial, there is a growing need for innovative methods to improve diagnosis and monitoring of these conditions. We hypothesize that identifying specific diagnostic biomarkers in human saliva, alongside traditional methods, could enhance clinical outcomes in managing both malignant and non-malignant oral pathologies. In this study, salivary levels of previously described biomarkers BDNF, IL-6, IL-8, IL-10, IL-15, IL-1RA, LIF, and TNF-α were compared among oral cancer patients, patients with OPMD, periodontal disease patients, and healthy volunteers. The results indicated that these cytokines have the potential to distinguish cancer cases, though they are less effective in differentiating between OPMD and periodontitis. Using pairs or groups of biomolecules could improve the ability to differentiate between different pathological entities. We found the following panel IL-6, IL-15, and IL-1RA to be particularly significant as diagnostic biomarkers for oral squamous cell carcinoma and OPMD.

Unveiling Biomarkers in Head and Neck Squamous Cell Carcinoma through Bioinformatics: The Role of SPP1 and KRT78.

Head and neck squamous cell carcinoma (HNSCC) is the most common form of head and neck cancer, ranking sixth in global cancer incidence. Identifying molecular drivers of tumorigenesis and metastasis is essential for early detection and treatment. This study analyzed gene expression profiles from three datasets (GSE6791, GSE29330, and GSE58911) to identify differentially expressed genes (DEGs) in HNSCC. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were employed to functionally annotate these DEGs. A protein-protein interaction (PPI) network was constructed for selecting hub genes using the STRING database. Finally, hub gene and protein expression levels were evaluated in patients with HNSCC, along with their association with overall survival. Our analysis identified twenty-eight co-DEGs comprising eight up-regulated and twenty down-regulated genes, primarily involved in extracellular matrix (ECM) organization, proteolysis, ECM disassembly, and keratinization processes. Furthermore, the PPI network revealed eight hub genes based on their high degree of connectivity. Notably, SPP1 demonstrated up-regulation, while KRT78 was down-regulated in HNSCC. Remarkably, the expression levels of these hub genes correlated with tumor grade, clinical cancer stage, and poor prognosis in HNSCC. Our findings hold significant clinical potential for early diagnosis and the development of novel therapeutic targets for patients with HNSCC.

Comparison of different histomorphological grading systems in vulvar squamous cell carcinoma.

Histopathological biomarkers of carcinomas and their prognostic relevance, such as Broder's grading system (based on the total number of undifferentiated cells) or Bryne's grading system (rating morphological features at the tumor invasive front), have been repeatedly and successfully put to test. Since most studies focus on head and neck cancers or oral carcinomas, for squamous cell carcinoma of the vulva, no standardized and agreed on pathological tumor grading system, yielding prognostic significance, could be determined so far. To determine prognostic associations of different grading systems with regard to groin lymph node metastasis, 73 cases of vulvar carcinomas (VC) were re-examined within our study and Broder's and Bryne's grading system individually performed. To sub-classify between HPV-associated or HPV-independent VC, immunohistochemical p16 stainings were performed. Statistical relationships were evaluated using Spearman correlation and logistic regression analysis, validation was achieved by employment of the likelihood ratio test (LRT) and assessment of ROC curves/AUC values. Within our cohort, Broder's grade I (40≈55%) and Bryne's grade II (48≈66%) were the most frequently assigned histological gradings. We determined a positive correlation of Bryne's grading with the extent of lymph node involvement in HPV-associated tumors and demonstrated the feasibility of Bryne's grading to predict the presence of carcinoma cells within groin lymph nodes (LRT p = 0.0066; AUC value≈0.91) in this cohort. On the other hand, our data suggest that especially HPV-independent tumors may not sufficiently be characterized by current standardly performed grading approaches. Since only Bryne's grading system correlated positively with lymph node involvement in HPV-associated squamous cell carcinoma of the vulva, we propose to include it by name next to the distinct tumor entity on the histopathological report, allowing not only the interpretation of its prognostic relevance but also future research attempts.

Novel miRNA markers and their mechanism of esophageal squamous cell carcinoma (ESCC) based on TCGA

MicroRNAs(miRNAs) are promising biomarkers for early esophageal squamous cell carcinoma (ESCC) detection and prognostic prediction. This study aimed to explore the potential biomarkers and molecular pathogenesis in the early diagnosis of ESCC. Firstly, 48 differentially expressed miRNAs (DEMs) and 1319 differentially expressed genes (DEGs) were identified between 94 ESCC tissues and 13 normal esophageal tissues in TCGA. From miRNA–mRNA regulatory network, there are 6558 target genes of the 48 DEMs, where 400 target genes are also among 1319 DEGs. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment indicate that the 400 DEGs significantly enriched in cell cycle, proteoglycans in cancer, p53 signaling pathway, protein digestion and absorption, transcriptional dysregulation in cancer, and oocyte meiosis. And there are 66 DEGs among these six biological pathways, which we called GO-DEGs. From miRNA–mRNA regulatory network, 32 DEMs regulated the 66 GO-DEGs, where 22 DEMs were verified by different types of experiments in ESCC tissues, cells, or serum from the literature. For the other novel 10 DEMs, single-factor Cox regression analysis show that only hsa-miR-34b-3p showed no significant correlation with the overall survival of ESCC patients. Finally, we obtained the novel 9 ESCC-related DEMs, where three are down-regulated, and six are up-regulated. We analyzed the expression trends of target genes for five miRNAs and identified three significantly different miRNAs (hsa-miR-205-3p, hsa-miR-452-3p, and hsa-miR-6499-3p) confirmed by qPCR. Moreover, the stage-specific miRNAs were also suggested. These three qPCR validated miRNAs are also specific to the early stages of ESCC: hsa-miR-452-3p is specific to Stage I, II and III; hsa-miR-205-3p is specific in Stage II and III; and hsa-miR-6499-3p is Stage II specific. They might be the potential biomarkers for ESCC stage diagnosis. This study identified three novel miRNA markers potentially related to the diagnosis of ESCC and participated in the occurrence and development of ESCC through cell cycle, proteoglycans in cancer, p53 signaling pathway, protein digestion and absorption, transcriptional dysregulation in cancer, and signaling pathway for oocyte meiosis.

Spectrum of Gastric Carcinoma in a Tertiary Care Centre with Special Reference to PDL1 and Cyciln D1

Introduction: Gastric carcinoma is the fifth most frequently diagnosed tumor and the fourth leading cause of death. They represent a heterogenous group of tumors with multifactorial etiologies, both environmental and genetic. PD-L1 is a transmembrane protein, and a positive PD-L1 expression in gastric carcinoma may be a predictive biomarker of the response to PD-1/PD-L1 immune checkpoint inhibitors. Cyclin D1 is an important cell cycle regulatory protein and involved in the carcinogenesis of various cancers including gastric cancer. Aims: The aim of this study is to find out the clinicopathological spectrum of gastric carcinoma and to show the immunohistochemical expression of Cyclin D1 and PD-L1 to evaluate their prognostic importance and their therapeutic impact. Study Design: It is an observational cross-sectional study conducted in the Department of Pathology in collaboration with the Department of Surgery, in a tertiary care hospital. Partial and total gastrectomies were selected from January 2021 to July 2022 in a tertiary care hospital. Clinical history was collected in the study proforma. Immunohistochemical staining for Cyclin D1 and PD-L1 was performed. Material and Methods: The clinicopathological parameters like age, gender, histological type, regional lymph node status, lymphovascular invasion, perineural invasion, and pathological stage were compared and correlated with Cyclin D1 and PD-L1 expression. Results: Positive Cyclin D1 and PDL1 expression were found in 50% and 70% cases of gastric carcinoma, respectively. Cyclin D1 expression showed statistically significant correlation with anemia, regional lymph node status, and lymphovascular invasion. PD-L1 and Cyclin D1 expression showed statistically significant association between them (p value-0.0006). Conclusion: Cyclin D1 and PDL1 are important biomarkers in gastric carcinoma. Their contribution towards prediction of progression and prognosis, along with newer therapeutic modalities, is of immense importace in these patients. A statistically significant correlation was found between these two markers.