Plasma GBP2 promoter methylation is associated with advanced stages in breast cancer

DNA hypo-methylation and expression of GBP4 induces T cell exhaustion in pancreatic cancer

Immunotherapy for pancreatic ductal carcinoma (PDAC) remains disappointing due to the repressive tumor microenvironment and T cell exhaustion, in which the roles of interferon-stimulated genes were largely unknown. Here, we focused on a typical interferon-stimulated gene, GBP4, and investigated its potential diagnostic and therapeutic value in pancreatic cancer. Expression analysis on both local samples and public databases indicated that GBP4 was one of the most dominant GBP family members present in the PDAC microenvironment, and the expression level of GBP4 was negatively associated with patient survival. We then identified DNA hypo-methylation in regulatory regions of GBP4 in PDAC, and validated its regulatory role on GBP4 expression via performing targeted methylation using dCas9-SunTag-DNMAT3A-sgRNA-targeted methylation system on selected DNA locus. After that, we investigated the downstream functions of GBP4, and chemotaxis assays indicated that GBP4 overexpression significantly improved the infiltration of CD8+T cells, but also induced upregulation of immune checkpoint genes and T cell exhaustion. Lastly, in vitro T cell killing assays using primary organoids suggested that the PDAC samples with high level of GBP4 expression displayed significantly higher sensitivity to anti-PD-1 treatment. Taken together, our studies revealed the expression patterns and epigenetic regulatory mechanisms of GBP4 in pancreatic cancer and clarified the effects of GBP4 on T cell exhaustion and antitumor immunology.

The function of guanylate binding protein 3 (GBP3) in human cancers by pan-cancer bioinformatics

As a guanylate binding protein (GBPs) member, GBP3 is immune-associated and may participate in oncogenesis and cancer therapy. Since little has been reported on GBP3 in this field, we provide pan-cancer bioinformatics to investigate the role of GBP3 in human cancers. The GBP3 expression, related clinical outcomes, immune infiltrates, potential mechanisms and mutations were conducted using tools including TIMER2.0, GEPIA2.0, SRING, DAVID and cBioPortal. Results showed an increased risk of high GBP3 in Brain Lower Grade Glioma (LGG) and Lung Squamous Cell Carcinoma (LUSC) and a decreased risk of GBP3 in Sarcoma (SARC) and Skin Cutaneous Melanoma (SKCM) (p ≤ 0.05). GBP3 was negatively correlated with CAFs in Esophageal Adenocarcinoma (ESCA) and positively correlated with CAFs in LGG, LUSC and TGCG (p ≤ 0.05). In addition, GBP3 was positively correlated with CD8+ T cells in Bladder Urothelial Carcinoma (BLCA), Cervical Squamous Cell Carcinoma (CESC), Kidney Renal Clear Cell Carcinoma (KIRC), SARC, SKCM, SKCM-Metastasis and Uveal Melanoma (UVM) (p ≤ 0.05). Potentially, GBP3 may participate in the homeostasis between immune and adaptive immunity in cancers. Moreover, the most frequent mutation sites of GBP3 in cancers are R151Q/* and K380N. This study would provide new insight into cancer prognosis and therapy.

A genome-wide cell-free DNA methylation analysis identifies an episignature associated with metastatic luminal B breast cancer

Breast cancers of the luminal B subtype are frequent tumors with high proliferation and poor prognosis. Epigenetic alterations have been found in breast tumors and in biological fluids. We aimed to profile the cell-free DNA (cfDNA) methylome of metastatic luminal B breast cancer (LBBC) patients using an epigenomic approach to discover potential noninvasive biomarkers. Plasma cfDNA was analyzed using the Infinium MethylationEpic array in a cohort of 14 women, including metastatic LBBC patients and nontumor controls. The methylation levels of cfDNA and tissue samples were validated with droplet digital PCR. The methylation and gene expression data of 582 primary luminal breast tumors and 79 nontumor tissues were obtained from The Cancer Genome Atlas (TCGA). We found an episignature of 1,467 differentially methylated CpGs that clearly identified patients with LBBC. Among the genes identified, the promoter hypermethylation of WNT1 was validated in cfDNA, showing an area under the ROC curve (AUC) of 0.86 for the noninvasive detection of metastatic LBBC. Both paired cfDNA and primary/metastatic breast tumor samples showed hypermethylation of WNT1. TCGA analysis revealed significant WNT1 hypermethylation in the primary tumors of luminal breast cancer patients, with a negative association between WNT1 methylation and gene expression. In this proof-of-principle study, we discovered an episignature associated with metastatic LBBC using a genome-wide cfDNA methylation approach. We also identified the promoter hypermethylation of WNT1 in cfDNA as a potential noninvasive biomarker for luminal breast cancer. Our results support the use of EPIC arrays to identify new epigenetic noninvasive biomarkers in breast cancer.

Identification of a predictive gene signature related to pyroptosis for the prognosis of cutaneous melanoma

Pyroptosis is a form of inflammatory programmed cell death. However, because of no specific molecular biomarker, pyroptosis has not been considered as a novel therapeutic method to treat cutaneous melanoma (CM). Here, we identified pyroptosis genes that associate with the prognosis of CM patients and constructed an effective model for the prognostic prediction of CM patients. To identify genes related to pyroptosis that are differentially expressed in CM, we obtained gene expression data of CM patients and normal skin tissues from the Cancer Genome Atlas and the Genotype-Tissue Expression databases, and used another cohort obtained from Gene Expression Omnibus database for validation. Three genes (BST2, GBP5, and AIM2) that were associated with prognosis were found and incorporated into our prognostic model. Furthermore, we divided the patients into 2 groups: a high-risk group and a low-risk group. Functional analyses indicated that our model was correlated with patient survival and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. Meanwhile, compared with other clinical characteristics, our model significantly improved the diagnostic accuracy. Gene function analysis revealed that pyroptosis genes BST2, GBP5, and AIM2 were differentially expressed in CM patients and positively associated with patient prognosis. Finally, a risk score was used to generate nomograms that displayed favorable discriminatory abilities for CM. In summary, our model could significantly predict the prognosis of CM patients and be used for the development of CM therapy.

Lower Expression of GBP2 Associated With Less Immune Cell Infiltration and Poor Prognosis in Skin Cutaneous Melanoma (SKCM)

Guanylate binding protein 2 (GBP2) could bind to guanine nucleotides (GMP, GDP, and GTP) and exhibits antiviral activity against influenza virus through the innate immune response. Some researchers have demonstrated that the value of GBP2 in predicting the prognosis of multiple cancers and the complex correlation with immune response. However, the correlation of GBP2 to prognosis and immune cell infiltration level were unknown in skin cutaneous melanoma (SKCM). The GBP2 expression in multiple cancers were evaluated through Tumor Immune Estimation Resource (TIMER) and Oncomine. We also evaluated the influence of GBP2 on overall survival in multiple caners through GEPIA, TIMER, and tissue microarray. The correlation between GBP2 expression level and immune cell or gene markers of immune infiltration level was explored on TIMER and GEPIA. Gene set enrichment analysis was performed using the TCGA dataset. The GBP2 expression level represented a significant reduction and the GBP2 expression was lower compared with the SKCM-Metastasis with P<0.01. Lower GBP2 expression was significantly correlated with the poor overall survival of SKCM patients. Simultaneously, higher GBP2 expression predicted the better SKCM-free survival with P=0.019. GBP2 expression was positively correlated with the infiltration cells of B-cell, CD8+ T-cell, CD4+ T-cell, macrophage, neutrophil, and dendritic cell in SKCM. And there was a significant negative correlation between the expression of GBP2 and DNA methylation in the cBioPortal database (P=3.39e-42). Gene set enrichment analysis revealed that GBP2 was closely correlated with multiple pathways of immune response in cancer. In conclusion, Lower expression of GBP2 associated with less immune cell infiltration and poor prognosis in SKCM and the high promoter methylation of GBP2 represented a promising biomarker for poor prognostication in SKCM.

Unraveling the Role of Guanylate-Binding Proteins (GBPs) in Breast Cancer: A Comprehensive Literature Review and New Data on Prognosis in Breast Cancer Subtypes

At least one member of the Guanylate-Binding Protein (GBP) family of large interferon-induced GTPases has been classified as both a marker of good prognosis and as a potential drug target to treat breast cancers. However, the activity of individual GBPs appears to not just be tumor cell type–specific but dependent on the growth factor and/or cytokine environment in which the tumor cells reside. To clarify what we do and do not know about GBPs in breast cancer, the current literature on GBP-1, GBP-2, and GBP-5 in breast cancer has been assembled. In addition, we have analyzed the role of each of these GBPs in predicting recurrence-free survival (RFS), overall survival (OS), and distance metastasis-free survival (DMFS) as single gene products in different subtypes of breast cancers. When a large cohort of breast cancers of all types and stages were examined, GBP-1 correlated with poor RFS. However, it was the only GBP to do so. When smaller cohorts of breast cancer subtypes grouped into ER+, ER+/Her2-, and HER2+ tumors were analyzed, none of the GBPs influenced RFS, OS, or DMSF as single agents. The exception is GBP-5, which correlated with improved RFS in Her2+ breast cancers. All three GBPs individually predicted improved RFS, OS, and DMSF in ER- breast cancers, regardless of the PR or HER2 status, and TNBCs.

Identification of Novel Molecular Therapeutic Targets and Their Potential Prognostic Biomarkers Based on Cytolytic Activity in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) attracts attention worldwide for its extremely high malignancy. A novel term cytolytic activity (CYT) has been introduced as a potential immunotherapy biomarker associated with counter-regulatory immune responses and enhanced prognosis in tumors. In this study, we extracted all datasets of SKCM patients, namely, RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, conducted differential expression analysis to yield 864 differentially expressed genes (DEGs) characteristic of CYT and used non-negative matrix factorization (NMF) method to classify molecular subtypes of SKCM patients. Among all genes, 14 hub genes closely related to prognosis for SKCM were finally screen out. Based on these genes, we constructed a 14-gene prognostic risk model and its robustness and strong predictive performance were further validated. Subsequently, the underlying mechanisms in tumor pathogenesis and prognosis have been defined from a number of perspectives, namely, tumor mutation burden (TMB), copy number variation (CNV), tumor microenvironment (TME), infiltrating immune cells, gene set enrichment analysis (GSEA) and immune checkpoint inhibitors (ICIs). Furthermore, combined with GTEx database and HPA database, the expression of genes in the model was verified at the transcriptional level and protein level, and the relative importance of genes in the model was described by random forest algorithm. In addition, the model was used to predict the difference in sensitivity of SKCM patients to chemotherapy and immunotherapy. Finally, a nomogram was constructed to better aid clinical diagnosis.

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype and lacks targeted treatment. It is diagnosed by the absence of immunohistochemical expression of several biomarkers, but this method still displays some interlaboratory variability. DNA methylome aberrations are common in BC, thereby methylation profiling could provide the identification of accurate TNBC diagnosis biomarkers. Here, we generated a signature of differentially methylated probes with class prediction ability between 5 non-neoplastic breast and 7 TNBC tissues (error rate = 0.083). The robustness of this signature was corroborated in larger cohorts of additional 58 non-neoplastic breast, 93 TNBC, and 150 BC samples from the Gene Expression Omnibus repository, where it yielded an error rate of 0.006. Furthermore, we validated by pyrosequencing the hypomethylation of three out of 34 selected probes (FLJ43663, PBX Homeobox 1 (PBX1), and RAS P21 protein activator 3 (RASA3) in 51 TNBC, even at early stages of the disease. Finally, we found significantly lower methylation levels of FLJ43663 in cell free-DNA from the plasma of six TNBC patients than in 15 healthy donors. In conclusion, we report a novel DNA methylation signature with potential predictive value for TNBC diagnosis.

GBP5 Repression Suppresses the Metastatic Potential and PD-L1 Expression in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype because of its high metastatic potential. Immune evasion due to aberrant expression of programmed cell death ligand 1 (PD-L1) has also been reported recently in metastatic TNBC. However, the mechanism underlying metastatic progression and PD-L1 upregulation in TNBC is still largely unknown. Here, we found that guanylate binding protein 5 (GBP5) is expressed in higher levels in TNBC tissues than in non-TNBC and normal mammary tissues and serves as a poorer prognostic marker in breast cancer patients. Transwell cultivation indicated that GBP5 expression is causally related to cellular migration ability in the detected TNBC cell lines. Moreover, the computational simulation of the gene set enrichment analysis (GSEA) program against the GBP5 signature generated from its coexpression with other somatic genes in TNBC revealed that GBP5 upregulation may be associated with the activation of interferon gamma (IFN-γ)-responsive and NF-κB-related signaling cascades. In addition, we found that the coexpression of GBP5 with PD-L1 was significantly positive correlation in TNBC tissues. Robustly, our data showed that GBP5 knockdown in TNBC cells harboring a higher GBP5 level dramatically suppresses the number of migrated cells, the activity of IFN-γ/STAT1 and TNF-α/NF-κB signaling axes, and the expression of PD-L1. Importantly, the signature combining a higher GBP5 and PD-L1 level predicted the shortest time interval of brain metastasis in breast cancer patients. These findings not only uncover the oncogenic function of GBP5 but also provide a new strategy to combat metastatic/immunosuppressive TNBC by targeting GBP5 activity.

GBP5 Serves as a Potential Marker to Predict a Favorable Response in Triple-Negative Breast Cancer Patients Receiving a Taxane-Based Chemotherapy

Pre-operative (neoadjuvant) or post-operative (adjuvant) taxane-based chemotherapy is still commonly used to treat patients with triple-negative breast cancer (TNBC). However, there are still no effective biomarkers used to predict the responsiveness and efficacy of taxane-based chemotherapy in TNBC patients. Here we find that guanylate-binding protein 5 (GBP5), compared to other GBPs, exhibits the strongest prognostic significance in predicting TNBC recurrence and progression. Whereas GBP5 upregulation showed no prognostic significance in non-TNBC patients, a higher GBP5 level predicted a favorable recurrence and progression-free condition in the TNBC cohort. Moreover, we found that GBP5 expression negatively correlated with the 50% inhibitory concentration (IC₅₀) of paclitaxel in a panel of TNBC cell lines. The gene knockdown of GBP5 increased the IC₅₀ of paclitaxel in the tested TNBC cells. In TNBC patients receiving neoadjuvant or adjuvant chemotherapy, a higher GBP5 level strongly predicted a good responsiveness. Computational simulation by the Gene Set Enrichment Analysis program and cell-based assays demonstrated that GBP5 probably enhances the cytotoxic effectiveness of paclitaxel via activating the Akt/mTOR signaling axis and suppressing autophagy formation in TNBC cells. These findings suggest that GBP5 could be a good biomarker to predict a favorable outcome in TNBC patients who decide to receive a taxane-based neoadjuvant or adjuvant therapy.

Screening and identification of potential biomarkers and therapeutic drugs in melanoma via integrated bioinformatics analysis

Melanoma is a highly aggressive malignant skin tumor with a high rate of metastasis and mortality. In this study, a comprehensive bioinformatics analysis was used to clarify the hub genes and potential drugs. Download the GSE3189, GSE22301, and GSE35388 microarray datasets from the Gene Expression Omnibus (GEO), which contains a total of 33 normal samples and 67 melanoma samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) approach analyze DEGs based on the DAVID. Use STRING to construct protein-protein interaction network, and use MCODE and cytoHubba plug-ins in Cytoscape to perform module analysis and identified hub genes. Use Gene Expression Profile Interactive Analysis (GEPIA) to assess the prognosis of genes in tumors. Finally, use the Drug-Gene Interaction Database (DGIdb) to screen targeted drugs related to hub genes. A total of 140 overlapping DEGs were identified from the three microarray datasets, including 59 up-regulated DEGs and 81 down-regulated DEGs. GO enrichment analysis showed that these DEGs are mainly involved in the biological process such as positive regulation of gene expression, positive regulation of cell proliferation, positive regulation of MAP kinase activity, cell migration, and negative regulation of the apoptotic process. The cellular components are concentrated in the membrane, dendritic spine, the perinuclear region of cytoplasm, extracellular exosome, and membrane raft. Molecular functions include protein homodimerization activity, calmodulin-binding, transcription factor binding, protein binding, and cytoskeletal protein binding. KEGG pathway analysis shows that these DEGs are mainly related to protein digestion and absorption, PPAR signaling pathway, signaling pathways regulating stem cells' pluripotency, and Retinol metabolism. The 23 most closely related DEGs were identified from the PPI network and combined with the GEPIA prognostic analysis, CDH3, ESRP1, FGF2, GBP2, KCNN4, KIT, SEMA4D, and ZEB1 were selected as hub genes, which are considered to be associated with poor prognosis of melanoma closely related. Besides, ten related drugs that may have therapeutic effects on melanoma were also screened. These newly discovered genes and drugs provide new ideas for further research on melanoma.