A novel strategy to identify candidate diagnostic and prognostic biomarkers for gastric cancer

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BACKGROUND

Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.

METHODS

We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.

RESULTS

WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.

CONCLUSIONS

HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Label-free and label-based electrochemical detection of disease biomarker proteins

Introduction

Biosensors, analytical devices integrating biological sensing elements with physicochemical transducers, have gained prominence as rapid and convenient tools for monitoring human health status using biochemical analytes. Due to its cost-effectiveness, simplicity, portability, and user-friendliness, electrochemical detection has emerged as a widely adopted method in biosensor applications. Crucially, biosensors enable early disease diagnosis by detecting protein biomarkers associated with various conditions. These biomarkers offer an objective indication of medical conditions that can be accurately observed from outside the patient.

Method

This review comprehensively documents both label-free and labelled detection methods in electrochemical biosensor techniques. Label-free detection mechanisms elicit response signals upon analyte molecule binding to the sensor surface, while labelled detection employs molecular labels such as enzymes, nanoparticles, and fluorescent tags.

Conclusion

The selection between label-free and labelled detection methods depends on various factors, including the biomolecular compound used, analyte type and biological binding site, biosensor design, sample volume, operational costs, analysis time, and desired detection limit. Focusing on the past six years, this review highlights the application of label-free and labelled electrochemical biosensors for detecting protein biomarkers of diseases.

Gastric cancer with brain metastasis: from molecular characteristics and treatment

Gastric cancer is one of the cancers with increasing incidence and ranks fourth globally among the most frequent causes of cancer-related mortality. Early gastric cancer is often asymptomatic or presents with atypical symptoms, and the majority of patients present with advanced disease upon diagnosis. Brain metastases are present in approximately 1% of gastric cancer patients at the time of diagnosis, which significantly contributed to the overall mortality of the disease worldwide. Conventional therapies for patients with brain metastases remain limited and the median overall survival of patients is only 8 months in advanced cases. Recent studies have improved our understanding of the molecular mechanisms underlying gastric cancer brain metastases, and immunotherapy has become an important treatment option in combination with radiotherapy, chemotherapy, targeted therapy and surgery. This review aims to provide insight into the cellular processes involved in gastric cancer brain metastases, discuss diagnostic approaches, evaluate the integration of immune checkpoint inhibitors into treatment and prognosis, and explore the predictive value of biomarkers in immunotherapy.

Single-cell analysis reveals one cancer-associated fibroblasts subtype linked to metastasis in breast cancer: MXRA5 as a potential novel marker for prognosis

Cancer-associated fibroblasts (CAFs) are prevalent in the tumor microenvironment of breast cancer, comprising a group of cell subpopulations with spatial, phenotypic, and functional heterogeneity. Due to the lack of specific markers for CAF subpopulations, their specific mechanisms in breast cancer remain unclear. We identified eight distinct CAF phenotypes in breast cancer using multiple single-cell RNA sequencing datasets and determined distinct transcription factors (TFs) of CAFs through SCENIC analysis. Our study highlights one CAF subtype in breast cancer, FN1+CAF2, associated with metastasis and macrophage polarization. We observed elevated FN1 expression in the stromal tissue of breast cancer patients. Furthermore, FN1 knockdown in CAFs reduced the migration ability of breast cancer cells. We identified a regulatory gene, MXRA5, in CAF2, which may play crucial roles in breast cancer. Our results indicated upregulated MXRA5 expression in breast cancer tissues and CAFs from patients with lymph node metastasis in the following experiment. Overall, our study reveals that the FN1+CAF2 subtype is associated with metastasis and suggests that MXRA5 may be a novel marker mediating the effects of CAF2 on breast cancer metastasis. This study enriches our understanding of CAF heterogeneity and offers new insights for treating breast cancer metastasis.

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan-Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

Bovine Omasum-Inspired Interfacial Carbon-Based Nanocomposite for Saliva Metabolic Screening of Gastric Cancer

Gastric cancer is one of the most common malignant digestive cancers, and its diagnostic has still faced challenges based on metabolic analysis due to complex sample pretreatment and low metabolite abundance. In this study, inspired by the structure of bovine omasum, we in situ synthesized a novel interfacial carbon-based nanocomposite of graphene supported nickel nanoparticles-encapsulated in the nitrogen-doped carbon nanotube (Ni/N-CNT/rGO), which was served as a novel matrix with enhanced ionization efficiency for the matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) saliva metabolic analysis of gastric cancer. Benefiting from its high sp² graphitic degree, large surface area, strong UV absorption, and rich active sites, Ni/N-CNT/rGO matrix exhibited excellent performances of reproducibility, coverage, salt-tolerance, sensitivity, and adsorption ability in MALDI-TOF MS. The differential scanning calorimetry (DSC) and thermal conversion behaviors explained the highly efficient LDI mechanism. Based on saliva metabolic fingerprints, Ni/N-CNT/rGO assisted LDI MS with cross-validation analysis could successfully distinguish gastric cancer patients from healthy controls through the screening of four potential biomarkers with an accuracy of 92.50%, specificity of 88.03%, and sensitivity of 97.12%. This work provided a fast and sensitive MS sensing platform for the metabolomics characterization of gastric cancer and might have potential value for precision medicine in the future.

System Analysis Based on Lipid-Metabolism-Related Genes Identifies AGT as a Novel Therapy Target for Gastric Cancer with Neoadjuvant Chemotherapy

Gastric cancer (GC) is one of the most common causes of cancer-related deaths worldwide, and chemotherapy is still a standard strategy for treating patients with advanced GC. Lipid metabolism has been reported to play an important role in the carcinogenesis and development of GC. However, the potential values of lipid-metabolism-related genes (LMRGs) concerning prognostic value and the prediction of chemotherapy responsiveness in GC remains unclear. A total of 714 stomach adenocarcinoma patients were enrolled from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Using univariate Cox and LASSO regression analyses, we developed a risk signature based on LMRGs that can distinguish high-GC-risk patients from low-risk patients with significant differences in overall survival. We further validated this signature prognostic value using the GEO database. The R package “pRRophetic” was applied to calculate the sensitivity of each sample from high- and low-risk groups to chemotherapy drugs. The expression of two LMRGs, AGT and ENPP7, can predict the prognosis and response to chemotherapy in GC. Furthermore, AGT significantly promoted GC growth and migration, and the downregulation of AGT enhanced the chemotherapy response of GC both in vitro and in vivo. Mechanistically, AGT induced significant levels of epithelial–mesenchymal transition (EMT) through the PI3K/AKT pathway. The PI3K/AKT pathway agonist 740 Y-P can restore the EMT of GC cells impaired by AGT knockdown and treatment with 5-fluorouracil. Our findings suggest that AGT plays a key role in the development of GC, and targeting AGT may help to improve the chemotherapy response of GC patients.

Effects of Exosomes on Tumor Bioregulation and Diagnosis

Exosomes are lipid bilayer vesicles in biological fluids, which can participate in biological processes by mediating intercellular communication and activating intracellular signaling pathways, especially cancerogenic processes, such as proliferation, metastasis, invasion, and immune regulation of cancer cells. Besides, cancer-derived exosomes are also involved in tumor diagnosis and therapy as biomarkers and nanotransport devices. This article reviews the latest research progress on the biological regulation and disease diagnosis of exosomes in tumors, with the aim of providing new ideas for the clinical treatment of cancers.

Prognostic and immunological role of SERPINH1 in pan-cancer

Background: The SERPINH1 gene plays a vital part in tumorigenesis and development, whereas its potential as an immunotherapy target is still unknown. Hence, this research aimed to probe the roles of SERPINH1 in human tumors.

Method: Using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, Oncomine, and SangerBox software, the pan-cancer expression of SERPINH1 and its correlation were systematically analyzed. SERPINH1 protein information was detected by the Human Protein Atlas (HPA) database and STRING database. The genomic alterations of SERPINH1 were studied using the c-BioPortal database. The influence of SERPINH1 on prognosis was analyzed using Kaplan–Meier plotter. The R package “clusterProfiler” was used for enrichment analysis to detect the role of SERPINH1. The TIMER2 database was used to further analyze the correlation between the immune cell infiltration score of TCGA samples and the expression of SERPINH1.

Results: SERPINH1 overexpression was related to worse survival status in pan-cancer. In addition, high expression of SERPINH1 was positively associated with tumor stage and poor prognosis. Moreover, SERPINH1 played an important role in tumor microenvironment and immune regulation. Our study revealed that SERPINH1 expression has a strong correlation with immune cell filtration, immune regulation, chemokines, and immune checkpoints.

Conclusion: Our research found that SERPINH1 was a risk factor and predictor of poor prognosis in various tumors. High expression of SERPINH1 may contribute to tumor immune-suppressive status. Also, SERPINH1 may become a potential immunotherapy target in pan-cancer.

Recent Advances in Nanomaterials for Diagnosis, Treatments, and Neurorestoration in Ischemic Stroke

Stroke is a major public health issue, corresponding to the second cause of mortality and the first cause of severe disability. Ischemic stroke is the most common type of stroke, accounting for 87% of all strokes, where early detection and clinical intervention are well known to decrease its morbidity and mortality. However, the diagnosis of ischemic stroke has been limited to the late stages, and its therapeutic window is too narrow to provide rational and effective treatment. In addition, clinical thrombolytics suffer from a short half-life, inactivation, allergic reactions, and non-specific tissue targeting. Another problem is the limited ability of current neuroprotective agents to promote recovery of the ischemic brain tissue after stroke, which contributes to the progressive and irreversible nature of ischemic stroke and also the severity of the outcome. Fortunately, because of biomaterials’ inherent biochemical and biophysical properties, including biocompatibility, biodegradability, renewability, nontoxicity, long blood circulation time, and targeting ability. Utilization of them has been pursued as an innovative and promising strategy to tackle these challenges. In this review, special emphasis will be placed on the recent advances in the study of nanomaterials for the diagnosis and therapy of ischemic stroke. Meanwhile, nanomaterials provide much promise for neural tissue salvage and regeneration in brain ischemia, which is also highlighted.

AGT May Serve as a Prognostic Biomarker and Correlated with Immune Infiltration in Gastric Cancer

Purpose

Angiotensinogen (AGT), as a component of the renin–angiotensin system (RAS), is associated with multiple risk factors for gastric cancer (GC). However, the relationship between AGT and tumor-infiltrating lymphocytes in GC remains elusive.

Methods

AGT expression was analyzed based on the Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier curve was employed to assess the role of AGT expression in gastric patients’ prognosis. The association between AGT expression and tumor immune infiltration was further evaluated via exploring Tumour Immune Estimation Resource (TIMER) and The Gene Expression Profiling Interactive Analysis (GEPIA). We also used multiple public databases to analyse the aberrant methylation of AGT, construct protein–protein interaction (PPI) and gene ontology (GO) analyses.

Results

AGT was overexpressed in GC tissues compared with normal gastric tissues (P<0.05). High AGT expression related with poorer overall survival of patients with GC, especially in advanced GC patients. Immune infiltration analysis revealed that AGT was associated with several immune cells (including B cells, CD4+ T cells, macrophages), and AGT expression was also associated with the markers of NK cells, TAMs, Tregs, and so on (all P<0.05). Methylation analysis indicated that hypomethylation may lead to abnormal upregulation of the AGT. GO analysis showed that AGT and its related genes were enriched in systemic arterial blood pressure by hormone, regulation of blood volume by renin-angiotensin, NIK/NF-kappaB signaling, ficolin-1-rich granule and so on.

Conclusion

AGT could act as a promising biomarker for prognosis and immune infiltration in GC.

Expression, regulation, and function of exosome-derived miRNAs in cancer progression and therapy

Exosomes are a novel class of intercellular signal modulators that contain a wide range of molecules and deliver information between cells and tissues. MicroRNAs (miRNAs), a type of regulatory non-coding RNA, are often incorporated into exosomes as signaling molecules. In this review, we discuss the expression of exosomal miRNAs from diverse origins such as tumor cells, solid tumor tissue, and biological fluids in various cancers (lung, breast, colorectal, liver, stomach, and pancreatic). We address the biological functions of exosome-derived miRNAs in processes such as tumor-cell proliferation, angiogenesis, metastasis, and chemoresistance in the tumor microenvironment. In particular, we discuss three oncogenic miRNAs, miR-21, miR-141, and miR-451, which occur within exosomes, in terms of gene regulation and intercellular communication. We consider therapeutic miRNA-based nanoparticles, which are widely expressed in tumors and show promise in drug therapy. The review assesses the wide-ranging evidence for using exosomal miRNAs as tumor markers in molecular diagnosis. Further, we consider the use of nanoparticle platforms to transport miRNAs, in the targeted treatment of disease and tumors.