Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Single-cell hdWGCNA reveals a novel diagnostic model and signature genes of macrophages associated with chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory system-related mortality worldwide. Although COPD is associated with immune regulation, its underlying mechanisms remain unclear.

METHODS

Cells from the single-cell RNA sequencing (scRNA-seq) datasets were subjected to clustering analysis and cell type identification to isolate immune cell subgroups specifically expressed in COPD. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify hub genes related to the immune cell subpopulations. Machine learning algorithms were applied to identify diagnostic genes in the immune cell subpopulations and construct clinical diagnostic models for COPD. In bulk RNA sequencing data, AUC curves were used to assess the stability of the diagnostic models in predicting COPD.

RESULTS

Through 2 rounds of clustering analysis, the macrophage subgroups 1, 2, 7, 11, and 13 which specifically expressed in COPD (COPD_Mφ) were identified. HdWGCNA analysis revealed a hub set of genes closely related to COPD_Mφ from black, blue, yellow, and brown modules. Nonnegative Matrix Factorization (NMF) analysis separated the COPD samples into 2 clusters, with significant increases in the infiltration of Monocytic_lineage, Myeloid_dendritic_cells, and Neutrophils in cluster 1 (P < 0.001). Univariate logistic regression and LASSO regression analyses identified 11 feature genes associated with COPD_Mφ, including CST3, LGALS3, CSTB, S100A10, CYBA, S100A11, ARPC3, FTH1, PFN1, MAN2B1, and RPL39. The RF and convolutional neural network (CNN) models constructed using these feature genes effectively distinguished between normal and COPD patients. Among them, S100A10, RPL39, and FTH1 exhibited differential expression between COPD patients and normal individuals and could serve as potential clinical diagnostic markers for COPD.

CONCLUSIONS

The study provides new insights into the immune mechanisms of COPD and lays the theoretical foundation for its future clinical diagnosis and personalized treatment.

A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy

BACKGROUND

The Golgi apparatus (GA) serves as the center of protein and lipid synthesis and modification within cells, playing a crucial role in regulating diverse cellular processes as a signaling hub. Dysregulation of GA function can give rise to a range of pathological conditions, including tumors. Notably, mutations in Golgi-associated genes (GARGs) are frequently observed in various tumors, and these mutations have been implicated in promoting tumor metastasis. However, the precise relationship between GARGs and glioma, a type of brain tumor, remains poorly understood. Therefore, the objective of this investigation was to assess the prognostic significance of GARGs in glioma and evaluate their impact on the immune microenvironment.

METHODS

The expression of GARGs was obtained from the TCGA and CGGA databases, encompassing a total of 1564 glioma samples (598 from TCGA and 966 from CGGA). Subsequently, a risk prediction model was constructed using LASSO regression and Cox analysis, and its efficacy was assessed. Additionally, qRT-PCR was employed to validate the expression of GARGs in relation to glioma prognosis. Furthermore, the association between GARGs and immunity, mutation, and drug resistance was investigated.

RESULTS

A selection of GARGs (SPRY1, CHST6, B4GALNT1, CTSL, ADCY3, GNL1, KIF20A, CHP1, RPS6, CLEC18C) were selected through differential expression analysis and Cox analysis, which were subsequently incorporated into the risk model. This model demonstrated favorable predictive efficiency, as evidenced by the area under the curve (AUC) values of 0.877, 0.943, and 0.900 for 1, 3, and 5-year predictions, respectively. Furthermore, the risk model exhibited a significant association with the tumor immune microenvironment and mutation status, as well as a diminished sensitivity to chemotherapy drugs. qRT-PCR analysis confirmed the up-regulation or down-regulation of the aforementioned genes in glioma.

CONCLUSION

The utilization of GARGs in our constructed model exhibits a high level of accuracy in prognosticating glioma and offers promising avenues for the development of therapeutic interventions targeting glioma.

RPL39 Was Associated With Sex Differences in Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) is a malignant cardiovascular disease with a complex etiology, in which several types of cells play important roles. Sex differences in disease susceptibility and survival have been observed in PAH patients, but few studies have analyzed the effect of changes in cell type and number on sex differences in PAH at the single-cell level. In this study, we performed a series of analyses on GSE169471 and GSE228644 datasets and found significant changes in the ratio of several types of cells in male PAH lung tissues. Surprisingly, we found that the ratio of macrophages in male PAH samples was 7 times higher than that in females. Consistently, the ratio of M1 macrophages was also significantly increased in male PAH samples. The different expression genes (DEGs) in macrophages were mainly involved in the ribosome pathway, which is closely related to cell proliferation. Inhibition of ribosomal protein L39 (RPL39), a core gene in the ribosome pathway, can inhibit macrophage proliferation and attenuate the sex differences in PAH. In conclusion, our study suggests that ribosome pathway-associated cell proliferation of macrophages might be associated with sex differences in PAH.

Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles

BACKGROUND/OBJECTIVES

Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters.

METHODS

Patients from the TCGA and CPTAC datasets (n = 114) were included in this study. Radiomic features including T1, T1 with contrast, T2, and FLAIR MRI sequences were extracted using PyRadiomics. Selected radiomic features were then clustered using ConsensusClusterPlus (k-means base algorithm and Euclidean distance), which iteratively subsamples and clusters 80% of the data to identify stable clusters by calculating the frequency in which each patient is a member of a cluster across iterations. Gene expression data (available for n = 69 patients) was analyzed using differential gene expression (DEG) and gene set enrichment (GSEA) approaches, after batch correction using ComBat-seq.

RESULTS

Three distinct clusters were identified based on the relative consensus matrix and cumulative distribution plots (Cluster 1, n = 25; Cluster 2, n = 46; Cluster 3, n = 43). No significant differences in patient demographic characteristics, MGMT methylation status, tumor location, or overall survival were identified across clusters. Differentially expressed genes were identified in Cluster 1, which have been previously associated with GBM prognosis, recurrence, and treatment sensitivity. GSEA of Cluster 1 showed an enrichment of genes upregulated for immune-related and DNA metabolism pathways and genes downregulated in pathways associated with protein and histone deacetylation. Clusters 2 and 3 exhibited fewer DEGs which failed to reach significance after multiple testing corrections.

CONCLUSIONS

Consensus clustering of radiomic features revealed unique gene expression profiles in the GBM cohort which likely represent subtle differences in tumor biology and radiosensitivity that are not visually discernible, underscoring the potential of radiomics to serve as a non-invasive alternative for identifying GBM molecular heterogeneity. Further investigation is still required to validate these findings and their clinical implications.

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.

Development and verification of a novel risk model related to ubiquitination linked with prognosis and therapeutic response in clear cell renal cell carcinoma

Increasing evidence highlights the important role of ubiquitination in cancer. The objective of our study is to establish a reliable marker for predicting clinical outcomes and treatment responses in patients with clear cell renal cell carcinoma (ccRCC) using genes related to ubiquitination (URGs). The URGs subtypes were identified using consensus clustering based on TCGA-KIRC, and a signature containing the prognostic differentially expressed genes of the subtypes was determined using LASSO and Cox regression analysis. To demonstrate the strength of the signature, verification analyses were performed on both E-MTAB-1980 and TCGA-KIRC test datasets. We developed a nomogram to enhance the effectiveness of our predictive tool. Risk genes expression was determined through RT-qPCR. Six genes were combined to create the URGs signature, which had a highly correlated with patient prognosis in patients with ccRCC. A nomogram was developed based on the URGs signature and clinicopathological characteristics. We found that the predictive power was substantially greater than the other individual predictors. Moreover, the study on the immune microenvironment revealed significant variations in the levels of immune cells and the expression of immune checkpoint genes among the groups categorized as high-risk and low-risk. Furthermore, it was found that immunotherapy yielded better outcomes in cohorts with low risk. The URGs signature might serve as a novel and powerful prognosis biomarker and offer a momentous reference for individualized treatment for patients in ccRCC.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning

Background

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma.

Methods

We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents.

Results

Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients.

Conclusions

The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Establishment of an in situ model to explore the tumor immune microenvironment in head and neck squamous cell carcinoma

OBJECTIVE

Establish an in situ model for investigating HNSCC, focusing on tumor growth, metastasis, and the immune microenvironment.

METHODS

Generated a monoclonal SCCVII-ZsGreen cell line through lentiviral transfection. Selected monoclonal lines with growth rates similar to the original SCCVII for in vivo tumorigenesis. Monitored tumor development and metastasis through fluorescence in vivo imaging. Employed immunohistochemistry to assess immune cell distribution in the tumor microenvironment.

RESULTS

SCCVII-ZsGreen exhibited comparable proliferation and in vivo tumorigenicity to SCCVII. In situ tumor formation on day 10, with cervical metastasis in C57BL/6 mice by day 16. No significant fluorescence signals in organs like liver and lungs, while SCCVII-ZsGreen presence confirmed in cervical lymph node metastases. Immunohistochemistry revealed CD4+ T, CD8+ T, B, and dendritic cells distribution, with minimal macrophages.

CONCLUSION

Our model is a valuable tool for studying HNSCC occurrence, metastasis, and immune microenvironment. It allows dynamic observation of tumor development, aids preclinical drug experiments, and facilitates exploration of the tumor immune contexture.

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

Arylsulfatase D is a prognostic biomarker that promotes glioma cells progression through JAK2/STAT3 pathway and M2 macrophage infiltration

Background

Arylsulfatase D (ARSD) belongs to the sulfatase family and plays a crucial role in maintaining the proper structure of bone and cartilage matrix. Although several researches have revealed the functions of ARSD in tumor progression, the prognostic value of ARSD in glioma and the related mechanisms have not been fully investigated.

Methods

We performed a pan-cancer analysis of ARSD, and investigated the relationship between expression of ARSD and overall survival (OS) in multiple glioma datasets. ROC curves and nomograms were created to investigate the predictive capacity of ARSD. Immune and analysis were conducted to investigate the mechanisms underlying the roles of ARSD in glioma. Glioma tissue samples were collected to verify the expression of ARSD in glioma, while the functions of ARSD were explored using cell experiment. M2 macrophage infiltration assay was used to determine the relation between ARSD and tumor immune microenvironment.

Results

Survival analysis indicated that individuals with high ARSD expression in glioma had a shorter survival time. Cox analysis showed that ARSD had a good ability for predicting prognosis in glioma. Immune analysis suggested that ARSD could regulate immune cell infiltration and affect the Cancer-Immunity Cycle to create an immunosuppressive environment. Combined with cell experiment and bioinformatic analysis, we found that ARSD can promote glioma progression through regulation of JAK2/STAT3 pathway and M2 macrophage infiltration.

Conclusion

Our study found that ARSD can promote glioma development by regulating immune microenvironment and JAK2/STAT3 signaling pathway, which provided a potential therapy target for glioma treatment.

Potential Candidate Genes for Therapeutic Targeting in Chronic Myeloid Leukemia: A Pilot Study

BACKGROUND

Chronic myeloid leukemia (CML) is a prevalent hematological malignancy known for the presence of the Philadelphia chromosome and activation of the BCR-Abl kinase activity. Although tyrosine kinase inhibitors are widely used as the standard treatment, resistance remains a concern among certain patients. This study aimed to investigate the gene expression profile of a group of CML patients in comparison to a control group in order to identify novel candidate genes associated with the disease.

METHODS

Whole transcriptome sequencing was performed, and gene expression levels were validated using quantitative real-time PCR. Additionally, single nucleotide and insertion/deletion variants were analyzed in the selected candidate genes among 10 CML patients and 4 healthy control subjects.

RESULTS

Analysis revealed a set of differentially expressed genes, whose up- or downregulation was further confirmed by qRT-PCR. Among the upregulated genes in the patient group were ribosomal protein like (RPL) members, specifically RPL9, RPL34, RPL36A, and RPL39, while downregulation was observed in CCDC170, LDB1, and SBF1 compared to the healthy subjects. Furthermore, gene variant studies identified novel genetic changes in these candidate genes, suggesting potential clinical significance in CML.

CONCLUSIONS

This study highlights RPL9, RPL34, RPL36A, RPL39, CCDC170, LDB1, and SBF1 as potential targets in CML. Additionally, it underscores the importance of investigating these genes and their variants in larger cohort studies to assess their clinical significance in CML patients.