An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance

Identification and validation of ion channels-related mRNA prognostic signature for glioblastomas

Glioblastomas (GBM) is a kind of malignant brain tumor with poor prognosis. Identifying new biomarkers is promising for the treatment of GBM. The mRNA-seq and clinical data were obtained from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas databases. The differentially expressed genes were identified using limma R package. The prognosis-related genes were screened out and a risk model was constructed using univariate, least absolute shrinkage and selection operator, and multivariate Cox analysis. Receiver operating characteristic curve was used to assess the efficiency of model. Kaplan-Meier survival curve was applied for the survival analysis. Mutation analysis was conducted using maftools package. The effect of immunotherapy was analyzed according to TIDE score, and the drug sensitivity analysis was performed. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis enrichment analyses were performed for the functional analysis. The regulatory network was constructed by STRING and Cytoscape software. RT-qPCR was performed to validate the expression of 3 hub genes in vitro. A risk model was constructed based on 3 ion channels related genes (gap junction protein beta 2 [GJB2], potassium voltage-gated channel subfamily h member 6 [KCNH6], and potassium calcium-activated channel subfamily n member 4 [KCNN4]). The risk score and hub genes were positively correlated with the calcium signaling pathway. Patients were divided into 2 groups based on the risk score calculated by 3 signatures. The infiltration levels of T cell, B lineage, monocytic lineage, and neutrophils were increased in high risk group, while TIDE score was decreased. IC50 of potential drugs for GBM treatment was elevated in the high risk group. Furthermore, GJB2, KCNH6, and KCNN4 were oncogenic, and GJB2 and KCNN4 were upregulated, while KCNH6 was downregulated in high risk group and GBM cells. The regulatory network showed that KCNH6 was targeted by more miRNA and transcription factors and KCNN4 interacted with more drugs. We constructed a three-signature risk model, which could effectively predict the prognosis of GBM development. Besides, KCNH6 and KCNN4 were respectively considered as the targets of molecular targeted treatment and chemotherapy.

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains incompletely understood. By analyzing single-cell RNA sequencing data from over 600,000 cells in gastric cancer (GSE163558 and GSE183904), colorectal cancer (GSE205506), and lung cancer (GSE207422), we identified neutrophil subsets in primary gastric cancer that are associated with the treatment response to ICIs. Specifically, we focused on neutrophils with high expression of CD44 (CD44_NEU), which are abundant during tumor progression and exert significant influence on the gastric cancer immune microenvironment. Machine learning analysis revealed 22 core genes associated with CD44_NEU, impacting inflammation, proliferation, migration, and oxidative stress. In addition, multiple immunofluorescence staining and gastric cancer spatial transcriptome data (GSE203612) showed a correlation between CD44_NEU and T-cell infiltration in gastric cancer tissues. A risk score model derived from seven essential genes (AQP9, BASP1, BCL2A1, PLEK, PDE4B, PROK2, and ACSL1) showed better predictive capability for patient survival compared to clinical features alone, and integrating these scores with clinical variables resulted in a prognostic nomogram. Overall, this study highlights the heterogeneity of TANs, particularly the CD44_NEU critical influence on immunotherapy outcomes, paving the way for personalized treatment strategies.

Deciphering COPS5 influence on immune infiltration and prognosis in head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is a widespread malignancy originating from the mucous epithelium of the oral cavity, pharynx, and larynx. Despite advances in diagnostic and therapeutic modalities, the prognosis of HNSCC remains challenging. This study investigates the intricate relationship among COPS5, immune infiltration patterns, and prognostic implications in HNSCC. Through comprehensive analyses of 519 HNSCC cases from TCGA and single-cell data from the GEO database, we utilize the CIBERSORT algorithm to discern immune cell dynamics influenced by COPS5 expression. Notably, Treg cells emerge as a central point in the interplay between COPS5 and immune modulation. Further analyses, encompassing differential gene expression, immune-related gene set enrichment, and protein-protein interaction networks, elucidate the molecular landscape associated with COPS5 in HNSCC. A prognostic risk model, incorporating CD27, TNFRSF4, FADD, and PSMD14, is formulated and validated across diverse datasets. The model demonstrates robust predictive power, underscoring its potential as a valuable prognostic tool. These genes, essential for immune regulation and cell cycle control, provide insights into the intricate mechanisms influencing HNSCC progression. In conclusion, this study not only reveals the impact of COPS5 on immune dynamics in HNSCC but also introduces a concise and effective prognostic model.

Molecular subtyping and the construction of a predictive model of colorectal cancer based on ion channel genes

PURPOSE

Colorectal cancer (CRC) is a highly heterogeneous malignancy with an unfavorable prognosis. The purpose of this study was to address the heterogeneity of CRC by categorizing it into ion channel subtypes, and to develop a predictive modeling based on ion channel genes to predict the survival and immunological states of patients with CRC. The model will provide guidance for personalized immunotherapy and drug treatment.

METHODS

A consistent clustering method was used to classify 619 CRC samples based on the expression of 279 ion channel genes. Such a method was allowed to investigate the relationship between molecular subtypes, prognosis, and immune infiltration. Furthermore, a predictive modeling was constructed for ion channels to evaluate the ion channel properties of individual tumors using the least absolute shrinkage and selection operator. The expression patterns of the characteristic genes were validated through molecular biology experiments. The effect of potassium channel tetramerization domain containing 9 (KCTD9) on CRC was verified by cellular functional experiments.

RESULTS

Four distinct ion channel subtypes were identified in CRC, each characterized by unique prognosis and immune infiltration patterns. Notably, Ion Cluster3 exhibited high levels of immune infiltration and a favorable prognosis, while Ion Cluster4 showed relatively lower levels of immune infiltration and a poorer prognosis. The ion channel score could predict overall survival, with lower scores correlated with longer survival. This score served as an independent prognostic factor and presented an excellent predictive efficacy in the nomogram. In addition, the score was closely related to immune infiltration, immunotherapy response, and chemotherapy sensitivity. Experimental evidence further confirmed that low expression of KCTD9 in tumor tissues was associated with an unfavorable prognosis in patients with CRC. The cellular functional experiments demonstrated that KCTD9 inhibited the proliferation, migration and invasion capabilities of LOVO cells.

CONCLUSIONS

Ion channel subtyping and scoring can effectively predict the prognosis and evaluate the immune microenvironment, immunotherapy response, and drug sensitivity in patients with CRC.

A prognostic model for Schistosoma japonicum infection-associated liver hepatocellular carcinoma: strengthening the connection through initial biological experiments

BACKGROUND

Numerous studies have shown that Schistosoma japonicum infection correlates with an increased risk of liver hepatocellular carcinoma (LIHC). However, data regarding the role of this infection in LIHC oncogenesis are scarce. This study aimed to investigate the potential mechanisms of hepatocarcinogenesis associated with Schistosoma japonicum infection.

METHODS

By examining chronic liver disease as a mediator, we identified the genes contributing to Schistosoma japonicum infection and LIHC. We selected 15 key differentially expressed genes (DEGs) using weighted gene co-expression network analysis (WGCNA) and random survival forest models. Consensus clustering revealed two subgroups with distinct prognoses. Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression identified six prognostic DEGs, forming an Schistosoma japonicum infection-associated signature for strong prognosis prediction. This signature, which is an independent LIHC risk factor, was significantly correlated with clinical variables. Four DEGs, including BMI1, were selected based on their protein expression levels in cancerous and normal tissues. We confirmed BMI1's role in LIHC using Schistosoma japonicum-infected mouse models and molecular experiments.

RESULTS

We identified a series of DEGs that mediate schistosomiasis, the parasitic disease caused by Schistosoma japonicum infection, and hepatocarcinogenesis, and constructed a suitable prognostic model. We analyzed the mechanisms by which these DEGs regulate disease and present the differences in prognosis between the different genotypes. Finally, we verified our findings using molecular biology experiments.

CONCLUSION

Bioinformatics and molecular biology analyses confirmed a relationship between schistosomiasis and liver hepatocellular cancer. Furthermore, we validated the role of a potential oncoprotein factor that may be associated with infection and carcinogenesis. These findings enhance our understanding of Schistosoma japonicum infection's role in LIHC carcinogenesis.

An angiogenesis-associated gene-based signature predicting prognosis and immunotherapy efficacy of head and neck squamous cell carcinoma patients

OBJECTIVES

To develop a model that can assist in the diagnosis and prediction of prognosis for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Data from TCGA and GEO databases were used to generate normalized gene expression data. Consensus Cluster Plus was used for cluster analysis and the relationship between angiogenesis-associated gene (AAG) expression patterns, clinical characteristics and survival was examined. Support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) analyzes and multiple logistic regression analyzes were performed to determine the diagnostic model, and a prognostic nomogram was constructed using univariate and multivariate Cox regression analyses. ESTIMATE, XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT algorithms were used to assess the immune microenvironment of HNSCC patients. In addition, gene set enrichment analysis, treatment sensitivity analysis, and AAGs mutation studies were performed. Finally, we also performed immunohistochemistry (IHC) staining in the tissue samples.

RESULTS

We classified HNSCC patients into subtypes based on differences in AAG expression from TCGA and GEO databases. There are differences in clinical features, TME, and immune-related gene expression between two subgroups. We constructed a HNSCC diagnostic model based on nine AAGs, which has good sensitivity and specificity. After further screening, we constructed a prognostic risk signature for HNSCC based on six AAGs. The constructed risk score had a good independent prognostic significance, and it was further constructed into a prognostic nomogram together with age and stage. Different prognostic risk groups have differences in immune microenvironment, drug sensitivity, gene enrichment and gene mutation.

CONCLUSION

We have constructed a diagnostic and prognostic model for HNSCC based on AAG, which has good performance. The constructed prognostic risk score is closely related to tumor immune microenvironment and immunotherapy response.

CXCL10-based gene cluster model serves as a potential diagnostic biomarker for premature ovarian failure

Objective

Premature ovarian failure (POF) is a disease with high clinical heterogeneity. Subsequently, its diagnosis is challenging. CXCL10 which is a small signaling protein involved in immune response and inflammation may have diagnostic potential in detection of premature ovarian insufficiency. Therefore, this study aimed to investigate CXCL10 based diagnostic biomarkers for POF.

Methods

Transcriptome data for POF was obtained from the Gene Expression Omnibus (GEO) database (GSE39501). Principal component analysis (PCA) assessed CXCL10 expression in patients with POF. The receiver operating characteristic (ROC) curve, analyzed using PlotROC, demonstrated the diagnostic potential of CXCL10 and CXCL10-based models for POF. Differentially expressed genes (DEGs) in the control group of POF were identified using DEbylimma. PlotVenn was used to determine the overlap between the POF-control group and the high-/low-expression CXCL10 groups. QuadrantPlot was employed to detect CXCL10-dysregulated genes in POF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were conducted on DEGs using RunMulti Group cluster Profiler. A POF model was induced with cisplatin (DDP) using KGN cells. RT-qPCR and Western blot were used to measure the expression of CXCL10, apoptosis-related proteins, and peroxisome proliferator-activated receptor (PPAR) signaling pathway-related proteins in this model, following siRNA-mediated silencing of CXCL10. Flow cytometry was employed to assess the apoptosis of KGN cells after CXCL10 downregulation.

Results

The expression of CXCL10 is dysregulated in POF, and it shows promising diagnostic potential for POF, as evidenced by an area under the curve value of 1. In POF, we found 3,362 up-regulated and 3,969 down-regulated DEGs compared to healthy controls, while the high- and low-expression groups of POF (comprising samples above and below the median CXCL10 expression) exhibited 1,304 up-regulated and 1,315 down-regulated DEGs. Among these, 786 DEGs consistently displayed dysregulation in POF due to CXCL10 influence. Enrichment analysis indicated that the PPAR signaling pathway was activated by CXCL10 in POF. The CXCL10-based model (including CXCL10, Itga2, and Raf1) holds potential as a diagnostic biomarker for POF. Additionally, in the DDP-induced KGN cell model, interfering with CXCL10 expression promoted the secretion of estradiol, and reduced apoptosis. Furthermore, CXCL10 silencing led to decreased expression levels of PPARβ and long-chain acyl-CoA synthetase 1 compared to the Si-NC group. These results suggest that CXCL10 influences the progression of POF through the PPAR signaling pathway.

Conclusion

The CXCL10-based model, demonstrating perfect diagnostic accuracy for POF and comprising CXCL10, Itga2, and Raf1, holds potential as a valuable diagnostic biomarker. Thus, the expression levels of these genes may collectively provide valuable diagnostic information for POF.

The role of potassium in depth profiling of the tumor border in bone-invasive oral cancer using laser-induced breakdown spectroscopy (LIBS): a pilot study

PURPOSE

Microscopic tumor spread beyond the macroscopically visible tumor mass in bone represents a major risk in surgical oncology, where the spatial complexity of bony resection margins cannot be countered with rapid bone analysis techniques. Laser-induced breakdown spectroscopy (LIBS) has recently been introduced as a promising option for rapid bone analysis. The present study aimed to use LIBS-based depth profiling based on electrolyte disturbance tracking to evaluate the detection of microscopic tumor spread in bone.

METHODS

After en bloc resection, the tumor-infiltrated mandible section of a patient's segmental mandibulectomy specimen was natively investigated using LIBS. Spectral and electrolytic depth profiles were analyzed across 30 laser shots per laser spot position in healthy bone and at the tumor border. For the histological validation of the lasered positions, the mandibular section was marked with a thin separating disc.

RESULTS

Solid calcium (Ca) from hydroxyapatite and soluble Ca from dissolved Ca can be reliably differentiated using LIBS and reflect the natural heterogeneity of healthy bone. Increased potassium (K) emission values in otherwise typically healthy bone spectra are the first spectral signs of tumorous bone invasion. LIBS-based depth profiles at the tumor border region can be used to track tumor-associated changes within the bone with shot accuracy based on the distribution of K.

CONCLUSION

Depth profiling using LIBS might enable the detection of microscopic tumor spread in bone. In the future, direct electrolyte tracking using LIBS should be applied to other intraoperative challenges in surgical oncology to advance rapid bone analysis by spectroscopic-optical techniques.