Potential Biomarker For Prognosis Research Articles

Exploring various carbon nanomaterials-based electrodes modified with polymelamine for the reagentless electrochemical immunosensing of Claudin18.2

Claudin18.2 (CLDN18.2) is a tight junction protein often overexpressed in various solid tumors, including gastrointestinal and esophageal cancers, serving as a promising target and potential biomarker for tumor diagnosis, treatment assessment, and prognosis. Despite its significance, no biosensor has been reported to date for the detection of CLDN18.2. Here, we present the inaugural immunosensor for CLDN18.2. In this study, an amine-rich conducting polymer of polymelamine (PM) was electrografted onto different carbon nanomaterial-based screen-printed electrodes (SPEs), including carbon (C), graphene (Gr), graphene oxide (GO), carbon nanotube (CNT), and carbon nanofiber (CNF) via cyclic voltammetry. A comparative study was performed to explore the best material for the preparation of the PM-modified electrodes to be used as in-situ redox substrate for the immunosensor fabrication. The surface chemistry and structural features of pristine and PM-deposited electrodes were analyzed using Raman and scanning electron microscopy (SEM) techniques. Our results showed that the PM deposited on Gr and CNT/SPEs exhibited the most significant and stable redox behavior in PBS buffer. The terminal amine moieties on the PM-modified electrode surfaces were utilized for immobilizing anti-CLDN18.2 monoclonal antibodies via N-ethyl-N′-(3-(dimethylamino)propyl)carbodiimide/N-hydroxysuccinimide chemistry to construct the electrochemical immunosensor platform. Differential pulse voltammetry-based immunosensing of CLDN18.2 protein on BSA/anti-CLDN18.2/PM-Gr/SPE and BSA/anti-CLDN18.2/PM-CNT/SPE exhibited excellent selectivity against other proteins such as CD1, PDCD1, and ErBb2. The limits of detection of these two immunosensor platforms were calculated to be 7.9 pg/mL and 0.104 ng/mL for the CNT and Gr immunosensors, respectively. This study demonstrated that the PM-modified Gr and CNT electrodes offer promising platforms not only for the reagentless signaling but also for covalent immobilization of biomolecules. Moreover, these platforms offer excellent sensitivity and selectivity for the detection of CLDN18.2 due to its enhanced stable redox activity. The immunosensor demonstrated promising results for the sensitive detection of CLDN18.2 in biological samples, addressing the critical need for early gastric cancer diagnosis.

Bioinformatics analysis and validation of mesenchymal stem cells related gene MT1G in osteosarcoma.

Osteosarcoma (OS) is a primary malignant bone tumor arising from mesenchymal cells. The standard clinical treatment for OS involves extensive tumor resection combined with neoadjuvant chemotherapy or radiotherapy. OS's invasiveness, lung metastasis, and drug resistance contribute to a low cure rate and poor prognosis with this treatment. Metallothionein 1G (MT1G), observed in various cancers, may serve as a potential therapeutic target for OS. OS samples in GSE33382 and TARGET datasets were selected as the test cohorts. As the external validation cohort, 13 OS tissues and 13 adjacent cancerous tissues from The Second Affiliated Hospital of Nanchang University were collected. Patients with OS were divided into high and low MT1G mRNA-expression groups; differentially expressed genes (DEGs) were identified as MT1G-related genes. The biological function of MT1G was annotated using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) and gene set enrichment analysis (GSEA). Gene expression correlation analysis and competing endogenous RNA (ceRNA) regulatory network construction were used to determine potential biological regulatory relationships of DEGs. Survival analysis assessed the prognostic value of MT1G. MT1G expression increased in OS samples and presented higher in metastatic OS compared with non-metastatic OS. Functional analyses indicated that MT1G was mainly associated with spliceosome. A ceRNA network with DEGs was constructed. MT1G is an effective biomarker predicting survival and correlated with increased recurrence rates and poorer survival. This research identified MT1G as a potential biomarker for OS prognosis, highlighting its potential as a therapy target.

Carbon nanotube-assisted detection of methylated cytosine: A DFT study for epigenetic biomarker analysis

DNA methylation plays a crucial role in cancer research, offering potential biomarkers for diagnosis and prognosis. We investigate the electronic and transport properties of carbon nanotubes (CNTs) as a device material to diagnose the tumor indicators, DNA 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), using density-functional theory (DFT). Our results reveal distinct electronic behaviors of 5mC and 5hmC compared to other nucleotides when interacting with CNT, suggesting its potential as diagnostic tool for tumor markers. Furthermore, electron transport and tunneling properties are examined using DFTB, demonstrating the ability to differentiate tumor indicators from other nucleotides based on current responses. Our findings highlight the ability of CNT-based biosensors for sensitive and efficient detection of DNA methylation patterns associated with cancer.

Label free quantitative proteomic profiling of serum samples of intellectually disabled young patients revealed dysregulation of complement coagulation and cholesterol cascade systems.

Intellectual disability is a heterogeneous disorder, diagnosed using intelligence quotient (IQ) score criteria. Currently, no specific clinical test is available to diagnose the disease and its subgroups due to inadequate understanding of the pathophysiology. Therefore, current study was designed to explore the molecular mechanisms involved in disease perturbation, and to identify potential biomarkers for disease diagnosis and prognosis. A total of 250 participants were enrolled in this study, including 200 intellectually disabled (ID) subjects from the subgroups (mild, moderate, and severe) with age and gender matched healthy controls (n = 50). Initially, IQ testing score and biochemical profile of each subject was generated, followed by label-free quantitative proteomics of subgroups of IQ and healthy control group through nano-LC/MS- mass spectrometry. A total of 310 proteins were identified, among them198 proteins were common among all groups. Statistical analysis (ANOVA) of the subgroups of ID showed 142 differentially expressed proteins, in comparison to healthy control group. From these, 120 proteins were found to be common among all subgroups. The remaining 22 proteins were categorized as exclusive proteins found only in disease subgroups. Furthermore, the hierarchical cluster analysis (HCL) of common significant proteins was also performed, followed by PANTHER protein classification and GO functional enrichment analysis. Results provides that the datasets of differentially expressed proteins, belong to the categories of immune / defense proteins, transfer carrier proteins, apolipoproteins, complement proteins, protease inhibitors, hemoglobin proteins etc., they are known to involvein immune system, and complement and coagulation pathway cascade and cholesterol metabolism pathway. Exclusively expressed 22 proteins were found to be disease stage specific and strong PPI network specifically those that have significant role in platelets activation and degranulation, such as Filamin A (FLNA). Furthermore, to validate the mass spectrometric findings, four highly significant proteins (APOA4, SAP, FLNA, and SERPING) were quantified by ELISA in all the study subjects. AUROC analysis showed a significant association of APOA4 (0.830), FLNA (0.958), SAP (0.754) and SERPING (0.600) with the disease. Apolipoprotein A4 (APOA4) has a significant role in cholesterol transport, and in modulation of glucose and lipid metabolism in the CNS. Similarly, FLNA has a crucial role in the nervous system, especially in the functioning of synaptic network. Therefore, both APOA4, and FLNA proteins represent good potential for candidate biomarkers for the diagnosis and prognosis of the intellectual disability. Overall, serum proteome of ID patients provides valuable information of proteins/pathways that are altered during ID progression.

Oncogenic role of RNA-binding protein GNL2 in glioma: Promotion of tumor development through enhancing protein synthesis.

RNA-binding proteins (RBPs) are aberrantly expressed in various diseases, including glioma. In the present study, the role and mechanism of RBPs in glioma were investigated. Differentially expressed genes (DEGs) in glioma were screened from public databases and overlapping genes between DEGs and RBPs were selected in a bioinformatics analysis to identify the hub gene. Next, evaluation of expression, survival analysis and cell experiments were performed to examine the impact of the hub gene on glioma. Through bioinformatics analysis, G protein nucleolar 2 (GNL2), programmed cell death 11 (PDCD11) and ribosomal protein S6 (RPS6) were identified as potential biomarkers in glioma prognosis and GNL2 was chosen as the hub gene for further investigation. GNL2 was increased in glioma tissues and related to poor survival outcomes. Cell experiments revealed that GNL2 knockdown inhibited glioma cell growth, migration and invasion. In addition, GNL2 was found to affect the overall protein synthesis of ribosomal protein L11 in glioma cells. In conclusion, GNL2, PDCD11 and RPS6 may serve as potential biomarkers in glioma prognosis. Importantly, GNL2 acts as an oncogene in glioma and it enhances protein synthesis to promote the development of brain glioma.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine.

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC. Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation. Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts. This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients. The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

Abstract PO3-24-07: The role of Hepatitis A Virus Cellular Receptor 1 (HAVCR1) in breast cancer and the impact on penetration of blood brain barrier

Abstract Background: Hepatitis A Virus Cellular Receptor 1 (HAVCR1) is associated with the biological behaviour of several cancers, and has been shown to regulate junctional complexes. However, its role in breast cancer remains unexplored. This study aimed to investigate the effects of HAVCR1 on human breast cancer. Methods: We explored the UALCAN web portal, which is linked to the TCGA database, to determine the expression of HAVCR1 in breast cancer and the Kaplan-Meier plotter to evaluate the correlation with the survival of the patients. HAVCR1 siRNA was used to knock down the HAVCR1 expression in MCF-7 and MDA-MB-231 cell lines. In vitro cell growth, wounding healing, and electric cell impedance sensing (ECIS for assessing cellular proliferation, migration and adhesion) assays were used. Transepithelial resistance (TEER) and paracellular permeability (PCP) were assessed to determine changes in the barrier function of human breast cancer cells. The expression of HAVCR1 and prospective interactive binding molecules of HAVCR1 protein was assessed using quantitative polymerase chain reaction (qPCR). Cellular response to docetaxel was evaluated and shown (IC50). The invasiveness of breast cancer cells through the human cerebral microvessel endothelial cells (hCMEC/D3) cell layers was measured using an in vitro transwell cell invasion assay. Results: HAVCR1 gene expression levels were significantly lower (P< 0.001) in breast cancer tissue compared with normal tissues. Patients with high expression of HAVCR1 had a significantly better relapse-free survival than low expression (P< 0.001). There was no significant difference in overall survival (OS) between the HAVCR1 high and the low expression groups (P=0.22). However, in subgroup analysis, high HAVCR1 expression was associated with better OS in luminal A (P=0.04). Knockdown (KD) HAVCR1 in breast cancer cells influence cell function by enhanced adhesion, proliferation, and migration compared to wild type (WT) cell lines. It was noted that loss of HAVCR1 rendered both MCF-7 and MDA-MB-231 cells resistant to docetaxel (IC50 for WT and HAVCR1 KD respectively being 0.86nM vs 1.75nM for MCF-7 and 1.35nM vs 15.48nM for MDA-MB-231). HAVCR1 KD increase adhesion to hCMEC/D3 cells (53.14±8.23 vs ±99.37±8.97 for MCF-7, P< 0.001; 109.5±97.56 vs 147.1±96.63 for MDA-MB-231, P=0.228, WT and HAVCR1 KD). In the cerebral endothelial cell invasion assay, we found that HAVCR1 KD resulted in a significantly increased invasion of the breast cancer cells through the endothelium (19.27±8.74 vs 33.58±16.81 for MDA-MB-231, P=0.0114, WT and HAVCR1 KD). For the barrier function, the knockdown led to a decrease in TEER and an increase in PCP when compared with WT cells in both cell lines. HAVCR1 KD cells showed decreased barrier function, together with reduced expression of Claudin-1/8/10, occludin, MarvelD3, Tricellulin and ZO1. Conclusions: HAVCR1 is a potential biomarker for breast cancer prognosis. HAVCR1 influences the integrity of barrier function by influence the gene expression of tight junction in breast cancer, and influence the penetration to blood brain barrier. Together, it suggests that HAVCR1 may be a potential therapeutic target in breast cancer. Citation Format: Xiaoshan Cao, Binbin Cong, Wen Jiang, Tracey Martin. The role of Hepatitis A Virus Cellular Receptor 1 (HAVCR1) in breast cancer and the impact on penetration of blood brain barrier [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-07.

Significance of NKX2-1 as a biomarker for clinical prognosis, immune infiltration, and drug therapy in lung squamous cell carcinoma.

This study was performed to determine the biological processes in which NKX2-1 is involved and thus its role in the development of lung squamous cell carcinoma (LUSC) toward improving the prognosis and treatment of LUSC. Raw RNA sequencing (RNA-seq) data of LUSC from The Cancer Genome Atlas (TCGA) were used in bioinformatics analysis to characterize NKX2-1 expression levels in tumor and normal tissues. Survival analysis of Kaplan-Meier curve, the time-dependent receiver operating characteristic (ROC) curve, and a nomogram were used to analyze the prognosis value of NKX2-1 for LUSC in terms of overall survival (OS) and progression-free survival (PFS). Then, differentially expressed genes (DEGs) were identified, and Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA) were used to clarify the biological mechanisms potentially involved in the development of LUSC. Moreover, the correlation between the NKX2-1 expression level and tumor mutation burden (TMB), tumor microenvironment (TME), and immune cell infiltration revealed that NKX2-1 participates in the development of LUSC. Finally, we studied the effects of NKX2-1 on drug therapy. To validate the protein and gene expression levels of NKX2-1 in LUSC, we employed immunohistochemistry(IHC) datasets, The Gene Expression Omnibus (GEO) database, and qRT-PCR analysis. NKX2-1 expression levels were significantly lower in LUSC than in normal lung tissue. It significantly differed in gender, stage and N classification. The survival analysis revealed that high expression of NKX2-1 had shorter OS and PFS in LUSC. The multivariate Cox regression hazard model showed the NKX2-1 expression as an independent prognostic factor. Then, the nomogram predicted LUSC prognosis. There are 51 upregulated DEGs and 49 downregulated DEGs in the NKX2-1 high-level groups. GO, KEGG and GSEA analysis revealed that DEGs were enriched in cell cycle and DNA replication.The TME results show that NKX2-1 expression was positively associated with mast cells resting, neutrophils, monocytes, T cells CD4 memory resting, and M2 macrophages but negatively associated with M1 macrophages. The TMB correlated negatively with NKX2-1 expression. The pharmacotherapy had great sensitivity in the NKX2-1 low-level group, the immunotherapy is no significant difference in the NKX2-1 low-level and high-level groups. The analysis of GEO data demonstrated concurrence with TCGA results. IHC revealed NKX2-1 protein expression in tumor tissues of both LUAD and LUSC. Meanwhile qRT-PCR analysis indicated a significantly lower NKX2-1 expression level in LUSC compared to LUAD. These qRT-PCR findings were consistent with co-expression analysis of NKX2-1. We conclude that NKX2-1 is a potential biomarker for prognosis and treatment LUSC. A new insights of NKX2-1 in LUSC is still needed further research.

PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.

Many studies have proven that microRNAs (miRNAs) can participate in a wide range of biological processes and can be considered as potential noninvasive biomarkers for disease diagnosis and prognosis. Therefore, many computational methods have been developed to identifying miRNA-disease associations, ultimately enhancing the efficiency of disease diagnosis and treatment. In this study, we also introduced a new computational method called PMDAGS, which predicts miRNA-disease associations by utilizing graph nonlinear diffusion convolution network and similarities. PMDAGS first calculates miRNA similarity and disease similarity based on miRNA-target interactions, disease-gene associations and known miRNA-disease associations, respectively. Next, we construct the initial feature of each miRNA (disease) by concatenating its final similarity vector with its known association vector. Based on the known miRNA-disease association network and the initial feature vector of each node, we further apply nonlinear diffusion graph convolution network model to extract the feature embedding vectors. Finally, we concatenate the feature embedding vectors of miRNA and disease and input them into a multi-layer perceptron to identify potential miRNA-disease associations. We conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV), and global leave-one-out cross validation (GLOOCV) on HMDD v2.0 and HMDD v3.2. PMDAGS achieves AUCs of 0.9222, 0.9228, and 0.9221 under 5CV, 10CV and GLOOCV on HMDD v2.0, respectively. In addition, PMDAGS also achieves AUC values of 0.9366, 0.9377, and 0.9376 under 5CV, 10CV and GLOOCV on HMDD v3.2, respectively. According to the experimental results, we can conclude that PMDAGS outperforms other compared methods and can effectively predict miRNA-disease associations.

EPIGENETIC MODIFICATIONS OF STRESS-RELATED DISORDERS: MECHANISMS, IMPLICATIONS AND THERAPEUTIC PERSPECTIVES

Stress-related disorders, encompassing conditions such as anxiety, depression, and post-traumatic stress disorder (PTSD), pose significant challenges to global mental health. While the etiology of these disorders involves a complex interplay of genetic, environmental, and psychosocial factors, emerging evidence suggests a pivotal role of epigenetic modifications in their pathogenesis. Epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNA-mediated regulation, dynamically regulate gene expression in response to stressors, thereby influencing brain function and behaviour. Alterations in these epigenetic processes contribute to aberrant stress responses, increased susceptibility to stress-related disorders, and individual differences in stress vulnerability and resilience. Moreover, epigenetic modifications can be transmitted across generations, highlighting the enduring impact of stress on mental health outcomes. Understanding the mechanisms underlying epigenetic regulation in stress-related disorders provides insights into disease pathogenesis and potential biomarkers for diagnosis and prognosis. Additionally, targeting epigenetic pathways represents a promising therapeutic approach for these disorders. Epigenetic drugs, such as DNA methyltransferase inhibitors and histone deacetylase inhibitors, hold potential for mitigating the adverse effects of stress on mental health. However, challenges such as off-target effects and lack of specificity necessitate further refinement of epigenetic-based therapies.

Gas6/TAM system as potential biomarker for multiple sclerosis prognosis.

The protein growth arrest-specific 6 (Gas6) and its tyrosine kinase receptors Tyro-3, Axl, and Mer (TAM) are ubiquitous proteins involved in regulating inflammation and apoptotic body clearance. Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system leading to progressive and irreversible disability if not diagnosed and treated promptly. Gas6 and TAM receptors have been associated with neuronal remyelination and stimulation of oligodendrocyte survival. However, few data are available regarding clinical correlation in MS patients. We aimed to evaluate soluble levels of these molecules in the cerebrospinal fluid (CSF) and serum at MS diagnosis and correlate them with short-term disease severity. In a prospective cohort study, we enrolled 64 patients with a diagnosis of clinical isolated syndrome (CIS), radiological isolated syndrome (RIS) and relapsing-remitting (RR) MS according to the McDonald 2017 Criteria. Before any treatment initiation, we sampled the serum and CSF, and collected clinical data: disease course, presence of gadolinium-enhancing lesions, and expanded disability status score (EDSS). At the last clinical follow-up, we assessed EDSS and calculated MS severity score (MSSS) and age-related MS severity (ARMSS). Gas6 and TAM receptors were determined using an ELISA kit (R&D Systems) and compared to neurofilament (NFLs) levels evaluated with SimplePlex™ fluorescence-based immunoassay. At diagnosis, serum sAxl was higher in patients receiving none or low-efficacy disease-modifying treatments (DMTs) versus patients with high-efficacy DMTs (p = 0.04). Higher CSF Gas6 and serum sAXL were associated with an EDSS<3 at diagnosis (p = 0.04; p = 0.037). Serum Gas6 correlates to a lower MSSS (r2 = -0.32, p = 0.01). Serum and CSF NFLs were confirmed as disability biomarkers in our cohort according to EDSS (p = 0.005; p = 0.002) and MSSS (r2= 0.27, p = 0.03; r2 = 0.39, p = 0.001). Results were corroborated using multivariate analysis. Our data suggest a protective role of Gas6 and its receptors in patients with MS and suitable severity disease biomarkers.

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches.

The maturation of B cells is a complex, multi-step process. During B cell differentiation, errors can occur, leading to the emergence of aberrant versions of B cells that, finally, constitute a malignant tumor. These B cell malignancies are classified into three main groups: leukemias, myelomas, and lymphomas, the latter being the most heterogeneous type. Since their discovery, multiple biological studies have been performed to characterize these diseases, aiming to define their specific features and determine potential biomarkers for diagnosis, stratification, and prognosis. The rise of advanced -omics approaches has significantly contributed to this end. Notably, proteomics strategies appear as promising tools to comprehensively profile the final molecular effector of these cells. In this narrative review, we first introduce the main B cell malignancies together with the most relevant proteomics approaches. Then, we describe the core studies conducted in the field and their main findings and, finally, we evaluate the advantages and drawbacks of flow cytometry, mass cytometry, and mass spectrometry for the profiling of human B cell disorders.

MiRNome Profiling Analysis Reveals Novel Hepatocellular Carcinoma Diagnostic, Prognostic and Treatment-Related Candidate Biomarkers: Post hoc Analysis of SORAMIC Trial

Introduction: Early diagnosis of hepatocellular carcinoma (HCC) as well as evaluation of prognosis and prediction of treatment efficacy remains challenging due to the missing specific non-invasive biomarkers. The aim of this study was to identify disease-specific microRNA (miRNA) patterns for diagnosis, prediction of prognosis, and treatment response in patients with HCC. Methods: The study population included 42 HCC patients from SORAMIC clinical trial: 22 patients received sorafenib monotherapy, 20 patients underwent 90Y radioembolization in combination with sorafenib. 20 individuals were included in the control group. HCC patients underwent collection of plasma samples before and 7–9 weeks after the beginning of the treatment. Isolation of circulating miRNAs, preparation of small RNA sequencing libraries and next-generation sequencing were performed. Association analysis for novel diagnostic, prognostic, and treatment-related candidate biomarkers was performed. Results: A total of 42 differentially expressed (16 up-regulated and 26 down-regulated) miRNAs were identified comparing baseline and control group plasma samples. hsa-miR-215-5p and hsa-miR-192-5p were down-regulated, while hsa-miR-483-5p and hsa-miR-23b-3p were up-regulated comparing baseline and 7–9 weeks post-sorafenib monotherapy samples. hsa-miR-215-5p was the sole down-regulated miRNA in the same combination therapy comparison. hsa-miR-183-5p, hsa-miR-28-3p, and hsa-miR-1246 were found to be significantly up-regulated comparing non-responders versus responders to sorafenib. High hsa-miR-215-5p expression was significantly associated with worse HCC patients’ prognosis. Conclusions: Systematic miRNA profiling of highly characterized samples from SORAMIC study revealed a subset of potential miRNA biomarkers for HCC diagnosis and prognosis of sorafenib-treated patients’ survival.

Serum aspartate aminotransferase, a novel potential biomarker of prognosis in extranodal natural killer/T cell lymphoma, nasal type.

Aspartate aminotransferase (AST), an indicator of liver cell damage, was related to the prognosis of certain malignant tumors. This study examined the predictive value of AST in patients with extranodal natural killer/T cell lymphoma (ENKTL). We reviewed 183 cases diagnosed with ENKTL and selected 26 U/L as the optimum cut-off value of AST. We used the univariate and multivariate Cox regression to compare the different AST groups' overall survival (OS) and progression-free survival (PFS). Prior to propensity score matching (PSM), Kaplan-Meier analysis showed that patients in the low AST subgroup had better OS and PFS than the high AST subgroup. Multivariate analysis revealed that AST was an independent indicator for prognosis. After PSM, the low AST subgroup maintained a significantly better OS and PFS than the high AST subgroup. AST might represent a significant prognostic marker for ENKTL patients.

CCDC58 is a potential biomarker for diagnosis, prognosis, immunity, and genomic heterogeneity in pan-cancer

Coiled-coil domain-containing 58 (CCDC58) is a member of the CCDC protein family. Similar to other members, CCDC58 exhibits potential tumorigenic roles in a variety of malignancies. However, there is no systematic and comprehensive pan-cancer analysis to investigate the diagnosis, prognosis, immune infiltration, and other related functions of CCDC58. We used several online websites and databases, such as TCGA, GTEx, UALCAN, HPA, CancerSEA, BioGRID, GEPIA 2.0, TIMER 2.0, and TISIDB, to extract CCDC58 expression data and clinical data of patients in pan-cancer. Then, the relationship between CCDC58 expression and diagnosis, prognosis, genetic alterations, DNA methylation, genomic heterogeneity, and immune infiltration level were determined. In addition, the biological function of CCDC58 in liver hepatocellular carcinoma (LIHC) was investigated. Pan-cancer analysis results showed that CCDC58 was differentially expressed in most tumors and showed excellent performance in diagnosis and prediction of prognosis. The expression of CCDC58 was highly correlated with genetic alterations, DNA methylation, and genomic heterogeneity in some tumors. In addition, the correlation analysis of CCDC58 with the level of immune infiltration and immune checkpoint marker genes indicated that CCDC58 might affect the composition of the tumor immune microenvironment. Enrichment analysis showed that CCDC58-related genes were mainly linked to mitosis, chromosome, and cell cycle. Finally, biological function experiments demonstrated that CCDC58 plays an important role in tumor cell proliferation and migration. CCDC58 was first identified as a pan-cancer biomarker. It may be used as a potential therapeutic target to improve the prognosis of patients in the future.

Prognostic and onco-immunological value of immune-related eRNAs-driven genes in lung adenocarcinoma

BackgroundWe aimed to comprehensively analyze the clinical value of immune-related eRNAs-driven genes in lung adenocarcinoma (LUAD) and find the potential biomarkers for prognosis and therapeutic response to improve the survival of this malignant disease.Materials and methodsPearson’s correlation analysis was performed to identify the immune-related eRNAs-driven genes. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were used to construct this prognostic risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to investigate the underlying molecular mechanism. The single sample gene set enrichment analysis (ssGSEA) algorithm was conducted to evaluate the immune status based on the signature. The quantitative real-time PCR (qRT-PCR) analysis was performed to evaluate the expression value of the signature genes between LUAD tissues and adjacent lung tissues.ResultsFive immune-related eRNAs-driven genes (SHC1, GDF10, CCL14, FYN, and NOD1) were identified to construct a prognostic risk signature with favorable predictive capacity. The patients with high-risk scores based on the signature were significantly associated with the malignant clinical features compared with those with low-risk scores. Kaplan–Meier analysis demonstrated that the sample in the low-risk group had a prolonged survival compared with those in the high-risk group. This risk signature was validated to have a promising predictive capacity and reliability in diverse clinical situations and independent cohorts. The functional enrichment analysis demonstrated that humoral immune response and intestinal immune network for IgA production pathway might be the underlying molecular mechanism related to the signature. The proportion of the vast majority of immune infiltrating cells in the high-risk group was significantly lower than that in the low-risk group, and the immunotherapy response rate in the low-risk group was significantly higher than that in the high-risk group. Moreover, BI-2536, sepantronium bromide, and ULK1 were the potential drugs for the treatment of patients with higher risk scores. Finally, the experiment in vivo and database analysis indicated that CCL14, FYN, NOD1, and GDF10 are the potential LUAD suppressor and SHC1 is a potential treatment target for LUAD.ConclusionAbove all, we constructed a prognostic risk signature with favorable predictive capacity in LUAD, which was significantly associated with malignant features, immunosuppressive tumor microenvironment, and immunotherapy response and may provide clinical benefit in clinical decisions.

Association between the lung microbiome and perioperative prognosis in lung transplant recipients.

Studies have confirmed that the lung microbiome of lung transplant recipients is altered and serves as a prognostic indicator for long-term mortality. Other studies reported that the lung microbiome affects host immunity and the transcriptome. However, the lung microbiome composition at the early post-transplant period following lung transplantation is unclear, and the relationship of the lung microbiome with pulmonary immunity and the host transcriptome is also not well understood. We hypothesize that changes in the lung microbiome composition in the early post-transplant period may have a predictive value for perioperative outcomes following lung transplantation and that the lung microbiome is correlated with pulmonary immunity and the host transcriptome. Thus, this prospective study aimed at observing the lung microbiome composition in the early post-transplant period and the impact of the lung microbiome on pulmonary cytokines and the host transcriptome. Our findings will help us gain a comprehensive understanding of the distribution and significance of the lung microbiome in the early post-transplant period. An observational study was conducted to identify the lung microbiome and the host transcriptome characteristics using next-generation sequencing. Luminex was employed for quantifying alveolar cytokines. Spearman's correlation analysis was utilized to assess the impact of the lung microbiome on pulmonary immunity and differentially expressed genes in patients who died perioperatively after lung transplantation. Patients with poor perioperative outcomes showed an increase in Mycoplasma and Arcobacter, a decrease of Gemella, and increased interleukin (IL)-10, IL-1β, and tumor necrosis factor (TNF)-α concentration. The lung microbiome correlates with lung immunity in lung transplant recipients. In the death group, the function of differentially expressed genes is associated with cell apoptosis, and promoting TNF production is upregulated. The lung microbiome is related to differentially expressed genes between the two groups. The lung microbiome and cytokines can be considered as potential biomarkers for early prognosis in lung transplant recipients. The lung microbiome is associated with both lung immunity and differentially expressed genes in lung transplant recipients.

Pan-cancer analyses of immunogenic cell death-derived gene signatures: Potential biomarkers for prognosis and immunotherapy.

Immunogenic cell death (ICD) is a type of regulated cell death that is capable of initiating an adaptive immune response. Induction of ICD may be a potential treatment strategy, as it has been demonstrated to activate the tumor-specific immune response. The biomarkers of ICD and their relationships with the tumor microenvironment, clinical features, and immunotherapy response are not fully understood in a clinical context. Therefore, we conducted pan-cancer analyses of ICD gene signatures across 33 cancer types from The Cancer Genome Atlas database. We identified key genes that had strong relationships with survival and the tumor microenvironment, contributing to a better understanding of the role of ICD genes in cancer therapy. In addition, we predicted therapeutic agents that target ICD genes and explored the potential mechanisms by which gemcitabine induce ICD. Moreover, we developed an ICD score based on the ICD genes and found it to be associated with patient prognosis, clinical features, tumor microenvironment, radiotherapy access, and immunotherapy response. A high ICD score was linked to the immune-hot phenotype, while a low ICD score was linked to the immune-cold phenotype. We uncovered the potential of ICD gene signatures as comprehensive biomarkers for ICD in pan-cancer. Our research provides novel insights into immuno-phenotypic assessment and cancer therapeutic strategies, which could help to broaden the application of immunotherapy to benefit more patients.

Identification of N7-methylguanosine-related miRNAs as potential biomarkers for prognosis and drug response in breast cancer

ObjectivesThe impact of N7-methylguanosine (m7G) on tumor progression and the regulatory role of microRNAs (miRNAs) in immune function significantly influence breast cancer (BC) prognosis. Investigating the interplay between m7G modification and miRNAs provides novel insights for assessing prognostics and drug responses in BC. Materials and methodsRNA sequences (miRNA and mRNA profiles) and clinical data for BC were acquired from the Cancer Genome Atlas (TCGA) database. A miRNA signature associated with 15 m7G in this cohort was identified using Cox regression and LASSO. The risk score model was evaluated using Kaplan-Meier and time-dependent ROC analysis, categorizing patients into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore potential pathways. The immune system, including scores, cell infiltration, function, and drug sensitivity, was examined and compared between high-risk and low-risk groups. A nomogram that combines risk scores and clinical factors was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore m7G-related miRNA signatures and immune cell relationships in the tumor microenvironment. Additionally, drug susceptibility was compared between risk groups. ResultsFifteen m7G-related miRNAs were independently correlated with overall survival (OS) in BC patients. Time-dependent ROC analysis yielded area under the curve (AUC) values of 0.742, 0.726, and 0.712 for predicting 3-, 5-, and 10-year survival rates, respectively. The Kaplan-Meier analysis revealed a significant disparity in OS between the high-risk and low-risk groups (p = 1.3e-6). Multiple regression identified the risk score as a significant independent prognostic factor. An excellent calibration nomogram with a C-index of 0.785 (95 % CI: 0.728–0.843) was constructed. In immune analysis, low-risk patients exhibited heightened immune function and increased responsiveness to immunotherapy and chemotherapy compared to high-risk patients. ConclusionThis study systematically analyzed m7G-related miRNAs and revealed their regulatory mechanisms concerning the tumor microenvironment (TME), pathology, and the prognosis of BC patient. Based on these miRNAs, a prognostic model and nomogram were developed for BC patients, facilitating prognostic assessments. These findings can also assist in predicting treatment responses and guiding medication selection.

Deciphering the role of TLR3 polymorphisms in oral squamous cell carcinoma pathogenesis: A case-control study.

Oral squamous cell carcinoma (OSCC) poses a significant global health burden, particularly prevalent in regions like India. Despite advancements in diagnostics, early detection of OSCC remains challenging, necessitating novel diagnostic modalities. Toll-like receptors (TLRs) and their polymorphisms have emerged as potential contributors to OSCC pathogenesis. This retrospective case-control study examined 120 individuals, including 60 OSCC cases and 60 healthy controls. Genotyping of TLR3 single-nucleotide polymorphisms (SNPs) rs3775290 and rs3775291 was conducted using TaqMan allelic discrimination real-time polymerase chain reaction. Functional consequence analysis and TLR3 expression profiling were performed to elucidate their role in OSCC pathogenesis. Significant associations were observed between TLR3 SNPs and OSCC susceptibility, particularly at loci rs3775290 and rs3775291. Functional consequence analysis revealed pathogenic mutations in TLR3 genes, potentially affecting protein structure and function. TLR3 overexpression was detected in OSCC lesions, implicating its involvement in disease progression. TLR3 polymorphisms play a pivotal role in OSCC pathogenesis, offering potential biomarkers for diagnosis and prognosis. Targeting TLR3-mediated pathways may hold promise in personalised OSCC management. Further research is warranted to elucidate the precise mechanisms underlying TLR3-mediated carcinogenesis in OSCC, facilitating the development of tailored therapeutic strategies.