GBP5 Expression Predicted Prognosis of Immune Checkpoint Inhibitors in Small Cell Lung Cancer and Correlated with Tumor Immune Microenvironment

The combination of single-cell and RNA sequencing analysis decodes the melanoma tumor microenvironment and identifies novel T cell-associated signature genes

Skin cutaneous melanoma (SKCM), a malignant melanocyte-derived skin cancer, potentially leads to fatal outcomes without effective treatment. The variability in immunotherapy responses among melanoma patients is significantly influenced by the intricate immune microenvironment, particularly due to the status of tumor T cells, encompassing their activity, exhaustion levels, and antigen recognition capabilities. This study utilized single-cell RNA sequencing (scRNA-seq) to analyze 34 melanoma samples from two public datasets (GSE215120 and GSE115978). Herein, we extracted 706 marker genes associated with immune checkpoint (ICP) therapy from these T cells, 509 markers of T cells from 11 melanoma tissues, and eventually identified 33 candidate genes. These genes underwent LASSO and COX regression analyses to identify the signature genes. Of the initial 33 candidate genes, we successfully isolated six distinct T cell-associated immunotherapy-related genes (IRTGs). Additionally, the computation of each patient risk score proved beneficial in evaluating the immune cell infiltration level and functions as an independent prognostic factor for melanoma patient survival. The risk score results revealed promising predictive outcomes in determining the response of melanoma patients to immunotherapy. Notably, our study is the first to reveal the potential correlation between signature gene PEB4B and the immune microenvironment in melaoma, which was explored with multiple immunofluorescence (IF) and Immune Infiltration Assessment. In a conclusion, our findings demonstrate the potential utility of a risk score dependent on signature genes as a predictive tool for assessing the prognosis and response to immunotherapeutic interventions in melanoma patients.

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

Guanylate binding protein 5 triggers NF-κB activation to foster radioresistance, metastatic progression and PD-L1 expression in oral squamous cell carcinoma

Radioresistance and metastasis are critical issues in managing oral squamous cell carcinoma (OSCC). Although immune checkpoint inhibitors (ICIs) has been recommended to treat OSCC, lacking useful biomarkers limited their anti-cancer effectiveness. We found that guanylate binding protein 5 (GBP5) is upregulated in primary tumors and associates with radioresistance in OSCC. GBP5 expression causally associated with cellular radioresistance and migration ability in the OSCC cell variants. GBP5 upregulation was examined to be correlated with NF-κB activation and programmed cell death-ligand 1 (PD-L1) elevation in OSCC samples. GBP5 knockdown was mitigated, but overexpression enhanced, NF-κB activity and PD-L1 expression in the OSCC cells. NF-κB inhibition by SN50 dramatically suppressed the GBP5-forested irradiation resistance, cellular migration ability and PD-L1 expression in OSCC cells. Importantly, GBP5 upregulation predicted a favorable outcome in cancer patients received ICI treatment. Our findings provide GBP5 as a useful biomarker to predict the anti-OSCC effectiveness of irradiation and ICIs.