Cancer ranks as the second leading cause of death globally and has surpassed cardiovascular diseases to become the primary cause of mortality in developed countries. Cancer stem cells (CSCs), which play crucial roles in cancer recurrence, metastasis, and drug resistance, have attracted significant attention in targeted therapeutic strategies. Aptamers, with unique three-dimensional structures capable of specifically recognizing the surface markers of CSCs, show promising potential in targeted drug delivery systems. Compared with conventional antibodies, aptamers are praised for small molecular weights, low production costs, and easy chemical modification. This review systematically summarizes recent advances in aptamer research targeting the surface markers of CSCs, with particular emphasis on aptamer-drug conjugate systems targeting the markers including EpCAM, CD133, CD44, and ABCG2. Both in vitro cellular studies and in vivo animal models have demonstrated the definite anti-cancer efficacy of aptamer-based drug delivery systems, which are of great significance to develop novel therapeutic strategies and improving the therapeutic effects of CSC-targeted treatment. Thus, aptamer-based drug delivery system has broad application prospects in the field of precise cancer treatment.
Humans ; Neoplastic Stem Cells/metabolism* ; Aptamers, Nucleotide/therapeutic use* ; Drug Delivery Systems/methods* ; Neoplasms/drug therapy* ; Biomarkers, Tumor/metabolism* ; Animals ; Epithelial Cell Adhesion Molecule ; AC133 Antigen ; Hyaluronan Receptors

In recent years, as a new omics field, metabolomics has been proved to be of great value in the study of the mechanism of occurrence and progression, the screening of new biomarkers and the development of novel therapeutic strategies in many diseases including tumors. In this review, we briefly summarized the research methods and techniques of metabolomics, and focused on the latest research progress of metabolomics in the pathogenesis of hematological malignancies represented by leukemia, lymphoma and multiple myeloma, screening of biomarkers for diagnosis and prognosis, and development of new therapeutic strategies. This article proposes the limitations of metabolomics and future research strategies, and provides a new exploration direction for accurate diagnosis and treatment as well as prognosis evaluation of hematological malignancies.
Humans ; Metabolomics/methods* ; Hematologic Neoplasms/diagnosis* ; Biomarkers, Tumor

BACKGROUND: Epidermal growth factor receptor (EGFR) sensitive mutation is one of the effective targets of targeted therapy for non-small cell lung cancer (NSCLC). However, due to the difficulty of obtaining some primary tissues and the economic factors in some underdeveloped areas, some patients cannot undergo traditional genetic testing. The aim of this study is to establish a machine learning (ML) model using non-invasive peripheral blood markers to explore the biomarkers closely related to EGFR mutation status in NSCLC and evaluate their potential prognostic value. METHODS: 2642 lung cancer patients who visited Jiangsu Cancer Hospital from November 2016 to May 2023 were retrospectively enrolled and finally 175 NSCLC patients with complete follow-up data were included in the study. The ML model was constructed based on peripheral blood indicators and divided into training set and test set according to the ratio of 8:2. Unsupervised learning algorithms were used for clustering blood features and mutual information method for feature selection, and an ensemble learning algorithm based on Shapley value was designed to calculate the contribution of each feature to the model prediction result. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the model. RESULTS: Through the feature extraction and contribution analysis of the predictive results of the interpretable ML model based on the Shapley value, the top ten indicators with the highest contribution were: pathological type, phosphorus, eosinophils, monocyte count, activated partial thromboplastin time, potassium, total bilirubin, sodium, eosinophil percentage, and total cholesterol. The area under the curve (AUC) of the model was 0.80. In addition, patients with hyponatremia and squamous cell carcinoma group had a poor prognosis (P<0.05). CONCLUSIONS The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnosis* ; Machine Learning ; Lung Neoplasms/diagnosis* ; Male ; Female ; Mutation ; Middle Aged ; ErbB Receptors/genetics* ; Prognosis ; Aged ; Retrospective Studies ; Adult ; Biomarkers, Tumor/genetics*

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Lung squamous cell carcinoma (LUSC) is an important pathological subtype of NSCLC. The complex immune escape mechanism limits the effectiveness of immunotherapy. Ficolin-3 (FCN3) is a crucial immunomodulatory molecule that regulates immune escape by remodeling the tumor microenvironment. However, the role of FCN3 in LUSC remains unclear. This study employed bioinformatics methods to analyze LUSC samples from The Cancer Genome Atlas (TCGA) database. The aim of this study was to explore the potential biological functions and prognostic significance of FCN3 in LUSC. METHODS: A pan-cancer analysis characterized the expression patterns and prognostic value of FCN3 across various cancer types. Simultaneously, the expression patterns of FCN3 in LUSC samples from the TCGA database and its relationship with prognosis were analyzed. The Nomogram model and somatic mutation analysis, differential expression analysis, correlation analysis, as well as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were constructed to explore the potential mechanisms of FCN3. Additionally, immune infiltration analysis, immune escape score (TIDE), and correlation analysis of immune-related molecules were used to reveal the regulatory role of high FCN3 levels on immunity in LUSC. Furthermore, the correlation between FCN3 expression characteristics and drug sensitivity was evaluated. Finally, in vitro experiments verified the expression characteristics of FCN3 in LUSC. RESULTS: The expression level of FCN3 in LUSC tissues was significantly lower than that in normal tissues. Patients with high FCN3 expression in LUSC had a poorer prognosis compared to those with low expression. Different expression levels of FCN3 were associated with the abundance of immune cell infiltration and immune cell dysfunction. It was also linked to the expression of immune checkpoints, immune stimulatory molecules, major histocompatibility complex (MHC) class molecules, and chemotherapy drug sensitivity. CONCLUSIONS High expression of FCN3 in LUSC is associated with poor prognosis and is linked to immune cell infiltration, immune-related pathways, and immune-related molecules. FCN3 may be a potential prognostic marker and a new target for immunotherapy in LUSC.
Humans ; Lung Neoplasms/immunology* ; Immunotherapy ; Biomarkers, Tumor/metabolism* ; Prognosis ; Lectins/metabolism* ; Carcinoma, Squamous Cell/immunology* ; Ficolins ; Gene Expression Regulation, Neoplastic

BACKGROUND: Lung cancer is one of the leading causes of cancer-related mortality worldwide, with above 80% of cases be non-small cell lung cancer (NSCLC), among which lung squamous cell carcinoma (LUSC) occupies a significant proportion. Although comprehensive cancer therapies have considerably improved the overall survival of patients, patients with advanced LUSC have a poorer prognosis. Therefore, there is a need for a biomarker to predict the progress of advanced LUSC in order to improve prognosis through early diagnosis. Previous studies have shown that miRNAs are differentially expressed in lung cancer tissues and play roles as potential oncogenes or tumor suppressors. The aim of this study is to identify differentially expressed miRNAs between early-stage and advanced-stage LUSC, and to establish a set of miRNAs that can predict the progress of advanced LUSC. METHODS: Clinical data and miRNA-related data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Bioinformatic methods were applied to analyze the data. Receiver operating characteristic (ROC) curves were plotted, and various online tools were used to predict target genes, with subsequent analysis of the potential biological mechanisms of these genes. RESULTS: A total of 58 differentially expressed miRNAs were identified between the experiment group and the control group. Seven miRNAs were selected for potential construction of a miRNA biomarker through LASSO regression, and based on the area under the curve (AUC) values of each miRNA, four of these miRNAs (miR-377-3p, miR-4779, miR-6803-5p, miR-3960) were ultimately chosen as biomarkers for predicting advanced LUSC. The AUC under the ROC curve for the combined four miRNAs was 0.865. Enrichment analysis showed that these target genes were involved in several pathways, including cancer-related pathways, mitogen-activated protein kinase (MAPK) signaling pathway, serine/threonine kinase, and tyrosine kinase signaling pathways. CONCLUSIONS The combined use of miR-377-3p, miR-4779, miR-6803-5p and miR-3960 provides a good predictive ability for the progress of advanced LUSC patients, with an AUC of 0.865.
Humans ; MicroRNAs/metabolism* ; Lung Neoplasms/metabolism* ; Biomarkers, Tumor/metabolism* ; Carcinoma, Squamous Cell/pathology* ; Gene Expression Regulation, Neoplastic ; Male ; Female ; Prognosis ; ROC Curve ; Middle Aged

Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

BACKGROUND: Depression among older adults is an important public health issue, and air and noise pollution have been found to contribute to exacerbation of depressive symptoms. This study examined the association of exposure to air and noise pollutants with clinically-newly-diagnosed depressive disorder. The mediating role of individual pro-inflammatory markers was explored. METHODS: We linked National Health Insurance claim data with 2998 healthy community-dwellers aged 55 and above who participated in the Healthy Aging Longitudinal Study between 2009 and 2013. Newly diagnosed depressive disorder was identified using diagnostic codes from the medical claim data. Pollutants were estimated using nationwide land use regression, including PM_2.5 and PM₁₀, carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, and road traffic noise. Cox proportional hazard models were employed to examine the association between pollutants and newly developed depressive disorders. The mediating effect of serum pro-inflammatory biomarkers on the relationship was examined. RESULTS: Among the 2998 participants, 209 had newly diagnosed depressive disorders. In adjusted Cox proportional hazard models, one interquartile range increase in PM_2.5 (8.53 µg/m³) was associated with a 17.5% increased hazard of developing depressive disorders. Other air pollutants and road traffic noise were not linearly associated with depressive disorder incidence. Levels of serum tumor necrosis factor receptor 1 mediated the relationship between PM_2.5 and survival time to newly onset depressive disorder. CONCLUSION PM_2.5 is related to an increased risk of newly developed depressive disorder among middle-aged and older adults, and the association is partially mediated by the pro-inflammatory marker TNF-R1.
Humans ; Particulate Matter/analysis* ; Male ; Female ; Middle Aged ; Longitudinal Studies ; Aged ; Incidence ; Air Pollutants/analysis* ; Environmental Exposure/adverse effects* ; Taiwan/epidemiology* ; Receptors, Tumor Necrosis Factor, Type I/blood* ; Proportional Hazards Models ; Biomarkers/blood* ; Depression/epidemiology* ; Aged, 80 and over ; Depressive Disorder/chemically induced* ; Risk Factors ; Air Pollution/adverse effects*

OBJECTIVE: To investigate the anti-tumor effects of cinobufacini (CINO) on hepatocellular carcinoma (HCC) induced by des-gamma-carboxy-prothrombin (DCP) and to uncover the underlying mechanisms. METHODS: The inhibitory effect of CINO on HCC cell proliferation was evaluated using the cell counting kit-8 method, and the apoptosis rate was quantified using flow cytometry. Immunofluorescence and Western blot analyses were used to investigate the differential expression of proteins associated with cell growth, apoptosis, migration, and invasion pathways after CINO treatment. The therapeutic potential of CINO for HCC was confirmed, and the possibility of combining cinobufacini with c-Met inhibitor for the treatment of primary HCC was further validated by in vivo experiments. RESULTS: Under the induction of DCP, CINO inhibited the activity of HCC cells, induced apoptosis, and inhibited migration and invasion. Upon the induction of DCP, CINO regulated c-Met activation and the activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) pathways. In a mouse model of HCC, CINO exhibited significant antitumor effects by inhibiting the phosphorylation of c-Met and the downstream PI3K/AKT and MEK/ERK pathways in tumor tissues. CONCLUSIONS CINO inhibited HCC cell growth, promoted apoptosis, and suppressed HCC cell invasion and migration by targeting c-Met and PI3K/AKT and MEK/ERK signaling pathways under DCP induction.
Carcinoma, Hepatocellular/drug therapy* ; Proto-Oncogene Proteins c-met/metabolism* ; Liver Neoplasms/drug therapy* ; Signal Transduction/drug effects* ; Animals ; Humans ; Cell Movement/drug effects* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Amphibian Venoms/therapeutic use* ; Cell Line, Tumor ; Neoplasm Metastasis ; Cell Survival/drug effects* ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Neoplasm Invasiveness ; Mice, Inbred BALB C ; Mice, Nude ; Mice ; Male ; Bufanolides/therapeutic use* ; Protein Precursors ; Prothrombin ; Biomarkers

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Multiple myeloma (MM) is a hematologically malignant clonal plasma cell disease. This study aims to explore the association between immunophenotypes and prognosis in patients with MM, to determine whether the expression of CD45 and CD200 is related to the prognosis of newly diagnosed MM (NDMM) patients, and to evaluate the significance of the combined expression of CD45 and CD200 in NDMM. METHODS: A total of 123 NDMM patients admitted to Shengjing Hospital of China Medical University from July 2015 to August 2019 were enrolled. Five key immunophenotypic markers (including CD38, CD138, CD45, CD56, and CD200) were screened through flow cytometry and identified using random forest analysis and univariate Cox regression analysis. Patients were divided into 3 groups: Group A, CD45 and CD200 double-positive; Group B, CD45 or CD200 single-positive; Group C, CD45 and CD200 double-negative. Kaplan-Meier curves were used to analyze overall survival (OS) and progression-free survival (PFS) across groups. Multivariate Cox regression was performed to evaluate prognostic factors, and a nomogram was constructed based on these results. RESULTS: The OS and PFS of single-positive groups for CD38, CD138, CD45, CD56, and CD200 were all shorter than those of their respective single-negative groups (all P<0.05). Significant differences were observed in OS (P<0.001) and PFS (P=0.001) among Groups A, B, and C. Group A had shorter OS and PFS (all P=0.001) compared to the Group B+C (cases from Group B and Group C were combined). CD45 and CD200 double-positive was an independent prognostic factor for NDMM [hazard ratio (HR)=2.178, 95% confidence interval (CI) 1.048 to 4.529; P=0.037]. The nomogram and calibration curves constructed from multivariate Cox regression analysis demonstrated good concordance (concordance index=0.706; 95% CI 0.661 to 0.751). CONCLUSIONS NDMM patients with double-positive expression of CD45 and CD200 have significantly shorter OS and PFS. Compared with the use of either marker alone, the combined assessment of CD45 and CD200 may provide better prognostic stratification for MM patients.
Humans ; Multiple Myeloma/metabolism* ; Male ; Female ; Middle Aged ; Antigens, CD/metabolism* ; Prognosis ; Leukocyte Common Antigens/metabolism* ; Aged ; Adult ; Immunophenotyping ; Nomograms ; Biomarkers, Tumor ; Clinical Relevance