Objective To explore the clinical and immunological significance of CCDC97 in hepatocellular carcinoma (HCC). Methods Clinical data and RNA sequencing results from HCC patients were retrieved from TCGA and ICGC databases. Bioinformatics analysis and in vitro experiments were performed to investigate the role of CCDC97 in HCC. Results The expression level of CCDC97 was elevated in HCC patients and HCC cells, closely associated with pathological features and prognosis. CCDC97 was identified as a novel prognostic biomarker. It is linked to the spliceosome pathway, which is significantly active in tumors and potentially promotes carcinogenesis. CCDC97 is also highly expressed in various immune cells and is associated with microenvironment. Furthermore, knocking down CCDC97 in vitro suppressed cell migration, invasion, and proliferation. Conclusion CCDC97 plays a critical role in HCC progression and the immune microenvironment, making it a potential target for prognosis and therapeutic intervention.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Tumor Microenvironment/genetics* ; Cell Movement/genetics* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics* ; Male

Objective To investigate the clinical relevance and diagnostic or prognostic value of ST3β-galactoside α-2, 3-sialyltransferase 1 (ST3GAL1) in glioma and to confirm its role in promoting malignant phenotypes. Methods Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the correlation between ST3GAL1 expression levels in glioma and clinical parameters to evaluate its diagnostic and prognostic value. The impact of ST3GAL1 on malignant phenotypes of glioma cells-including proliferation, cell cycle progression, apoptosis, and invasion was further validated through ST3GAL1 knockdown experiments. Results The expression level of ST3GAL1 was significantly higher in glioma tissues compared to healthy brain tissues and showed a strong correlation with clinical characteristics of glioma patients. Survival analysis and receiver operating characteristic (ROC) curve demonstrated that ST3GAL1 could serve as a potential diagnostic and prognostic biomarker for glioma. Knockdown of ST3GAL1 suppressed proliferation, invasion, and migration capabilities of glioma cell lines, and induced G1-phase cell cycle arrest. Conclusion ST3GAL1 promotes malignant phenotypes in glioma and plays a critical role in its malignant progression, suggesting its potential as a biomarker for glioma diagnosis and prognosis.
Humans ; Sialyltransferases/metabolism* ; Glioma/diagnosis* ; Cell Proliferation/genetics* ; Cell Line, Tumor ; Brain Neoplasms/enzymology* ; beta-Galactoside alpha-2,3-Sialyltransferase ; Disease Progression ; Prognosis ; Cell Movement/genetics* ; Apoptosis/genetics* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/metabolism* ; Middle Aged

Objective TXN is a thioredoxin (TXN) that participates in many redox reactions and plays a crucial role in various signaling pathways. However, the role of TXN in many cancers is still unclear. The objective of this study is to investigate and visualize the diagnostic, prognostic, and immunological implications of TXN expression across various cancer types. Methods The clinical data were downloaded from the cancer genome mapping project(TCGA) database to analyze the expression level of TXN in pan cancer, and the expression level was preliminarily verified by human protein mapping (HPA)(https://www.proteinatlas.org/)database. The ESTIMATE algorithm and CIBERSORT algorithm were applied to calculate the correlation between TXN expression and immune cell infiltration. The correlation between TXN and microsatellite instability (MSI) and tumor mutation burden (TMB) was analyzed using Spearman method. Gene Set Enrichment Analysis (GSEA) is used for gene biology functional analysis and sensitivity analysis of genes to pan cancer therapeutic drugs. Results TXN is highly expressed in most malignant tumors. The high expression of TXN is associated with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression free interval (PFI) in various cancers. Moreover, TXN expression is associated with TMB, MSI, tumor microenvironment, chemotherapy sensitivity and so on. Conclusion TXN may become a potential prognostic biomarker in pan cancer, providing strong theoretical basis for future tumor diagnosis and prognosis judgment. The retinoic acid-inducible gene-I (RIG-I)-like receptor signaling pathway, Toll-like receptor (TLR) signaling pathway, and nucleotide binding oligomerization domain (NOD)-like receptor signaling pathway may be crucial pathways through which TXN influences tumor immunity.
Humans ; Prognosis ; Neoplasms/diagnosis* ; Thioredoxins/metabolism* ; Microsatellite Instability ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics* ; Mutation ; Tumor Microenvironment

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*

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

Stathmin 1 (STMN1) is a microtubule-binding cytoplasmic phosphoprotein that promotes microtubule depolymerization or inhibits microtubule assembly, thereby regulating cytoskeletal organization and cell cycle progression. STMN1 is upregulated in a variety of malignant tumors, where it drives proliferation, invasion, metastasis, and angiogenesis through classic pathways such as nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), and ferroptosis. STMN1 can also modulate the function of immune cells, thereby influencing antitumor immunity. Clinical data show that its high expression correlates positively with tumor drug resistance and poor prognosis, suggesting that STMN1 has potential as a tumor biomarker and therapeutic molecular target with important clinical significance.
Humans ; Stathmin/metabolism* ; Neoplasms/genetics* ; Biomarkers, Tumor/metabolism* ; NF-kappa B/metabolism* ; Cell Proliferation ; Drug Resistance, Neoplasm

Mitochondria are the convergence point of multiple pathways that trigger programmed cell death (PCD). Mitochondrial-associated PCD (mtPCD) is involved in the pathogenesis of several diseases. However, the role of mtPCD in the prognostic prediction of cancers including non-small-cell lung cancer (NSCLC) remains to be investigated. Here, 12 mtPCD patterns were analyzed in transcriptomics, genomics, and clinical data collected from 4 datasets containing 977 patients. A risk-score assessment system containing 18 genes was established. We found that NSCLC patients with a high-risk score had a poorer prognosis. A nomogram was constructed by incorporating the risk score with clinical features. The risk score was further associated with clinicopathological information, tumor-mutation frequency, and immunotherapy responses. NSCLC patients with a high risk score had more Treg cells infiltration. However, these patients had higher tumor-mutation burden scores and may be more sensitive to immunotherapy. Moreover, receptor-interacting serine/threonine protein kinase 2 (RIPK2) was selected from mtPCD gene model for validation. We found that RIPK2 exhibited oncogenic function, and its expression level was inversely associated with the overall survival of NSCLC. Taken together, our results indicated the accuracy and practicability of the mtPCD gene model and RIPK2 in predicting the prognosis of NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Prognosis ; Male ; Female ; Nomograms ; Middle Aged ; Mitochondria/metabolism* ; Apoptosis/genetics* ; Mutation ; Biomarkers, Tumor/genetics* ; Aged

OBJECTIVE: Circadian rhythm disruption (CRD) is a risk factor that correlates with poor prognosis across multiple tumor types, including hepatocellular carcinoma (HCC). However, its mechanism remains unclear. This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity. METHODS: To quantify CRD, the HCC CRD score (HCCcrds) was developed. Using machine learning algorithms, we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort (n = 369), and the robustness of this method was validated. Furthermore, we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes. RESULTS: We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis. The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis, higher pathological grade, and advanced clinical stages, while the CRD-related subtype with low HCCcrds had better clinical outcomes. We also identified novel biomarkers for each subtype, such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1. CONCLUSION We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers. Within these groups greater HCCcrds was associated with worse prognosis. This approach has the potential to improve prediction of an individual's prognosis, guide precision treatments, and assist clinical decision making for HCC patients. Please cite this article as: Li XJ, Chang L, Mi Y, Zhang G, Zhu SS, Zhang YX, et al. Integrated-omics analysis defines subtypes of hepatocellular carcinoma based on circadian rhythm. J Integr Med. 2025; 23(4): 445-456.
Humans ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Circadian Rhythm/genetics* ; Prognosis ; Male ; Female ; Biomarkers, Tumor/genetics* ; Middle Aged ; Machine Learning ; Computational Biology

OBJECTIVE: To explore the correlation between chromosome 8 open reading frame 76 (C8orf76) and cyclin-dependent kinase 4 (CDK4) and the potential predictive effect of C8orf76 and CDK4 on the prognosis of colorectal cancer (CRC). METHODS: We constructed a protein-protein interaction network of C8orf76-related genes and analyzed the prognostic signatures of C8orf76 and CDK4. Clinicopathological features of C8orf76 and CDK4 were visualized using a nomogram. RESULTS: C8orf76 and CDK4 levels were positively correlated in two independent human CRC cohorts ( n = 83 and n = 597). A consistent positive correlation was observed between C8orf76 and CDK4 expression in the CRC cell lines. The nomogram included prognostic genes (C8orf76 and CDK4) and pathological N and M stages. The concordance index (C-index) in our cohort was 0.776, which suggests that the ability of the indicators to predict the overall survival of patients with CRC in our cohort was strong. CONCLUSION We found that C8orf76 was positively correlated with CDK4 in both the cohorts as well as in CRC cell lines. Therefore, C8orf76 and CDK4 can be used as potential biomarkers to predict the prognosis of CRC.
Humans ; Colorectal Neoplasms/diagnosis* ; Cyclin-Dependent Kinase 4/metabolism* ; Prognosis ; Male ; Female ; Middle Aged ; Biomarkers, Tumor/genetics* ; Aged ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic

Humans ; Colorectal Neoplasms/pathology* ; Pathology, Molecular/methods* ; Biomarkers, Tumor/genetics* ; Genetic Testing ; China