Interferon-inducible guanylate binding protein (GBP2) is associated with better prognosis in breast cancer and indicates an efficient T cell response

Identification of hub genes related to metastasis and prognosis of osteosarcoma and establishment of a prognostic model with bioinformatic methods

Osteosarcoma (OS) is the most common primary malignant bone tumor occurring in children and adolescents. Improvements in our understanding of the OS pathogenesis and metastatic mechanism on the molecular level might lead to notable advances in the treatment and prognosis of OS. Biomarkers related to OS metastasis and prognosis were analyzed and identified, and a prognostic model was established through the integration of bioinformatics tools and datasets in multiple databases. 2 OS datasets were downloaded from the Gene Expression Omnibus database for data consolidation, standardization, batch effect correction, and identification of differentially expressed genes (DEGs); following that, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs; the STRING database was subsequently used for protein-protein interaction (PPI) network construction and identification of hub genes; hub gene expression was validated, and survival analysis was conducted through the employment of the TARGET database; finally, a prognostic model was established and evaluated subsequent to the screening of survival-related genes. A total of 701 DEGs were identified; by gene ontology and KEGG pathway enrichment analyses, the overlapping DEGs were enriched for 249 biological process terms, 13 cellular component terms, 35 molecular function terms, and 4 KEGG pathways; 13 hub genes were selected from the PPI network; 6 survival-related genes were identified by the survival analysis; the prognostic model suggested that 4 genes were strongly associated with the prognosis of OS. DEGs related to OS metastasis and survival were identified through bioinformatics analysis, and hub genes were further selected to establish an ideal prognostic model for OS patients. On this basis, 4 protective genes including TPM1, TPM2, TPM3, and TPM4 were yielded by the prognostic model.

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.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction

Background

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.

Methods

Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.

Results

Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.

Conclusion

The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

GBP2 is a prognostic biomarker and associated with immunotherapeutic responses in gastric cancer

BACKGROUND

The interferon-induced protein known as guanylate-binding protein 2 (GBP2) has been linked to multiple different cancer types as an oncogenic gene. Although the role of GBP2 in cancer has been preliminarily explored, it is unclear how this protein interacts with tumor immunity in gastric cancer.

METHODS

The expression, prognostic value, immune-correlations of GBP2 in gastric cancer was explored in multiple public and in-house cohorts. In addition, the pan-cancer analysis was performed to investigate the immunological role of GBP2 based on The Cancer Genome Atlas (TCGA) dataset, and the predictive value of GBP2 for immunotherapy was also examined in multiple public cohorts.

RESULTS

GBP2 was highly expressed in tumor tissues and associated with poor prognosis in gastric cancer. In addition, GBP2 was associated with the immune-hot phenotype. To be more specific, GBP2 was positively related to immuno-modulators, tumor-infiltrating immune cells (TIICs), immunotherapy biomarkers, and even well immunotherapeutic response. In addition to gastric cancer, GBP2 was expected to be an indicator of high immunogenicity in most cancer types. Importantly, GBP2 could predict the immunotherapeutic responses in at least four different cancer types, including melanoma, urothelial carcinoma, non-small cell lung cancer, and breast cancer.

CONCLUSIONS

To sum up, GBP2 expression is a promising pan-cancer biomarker for estimating the immunological characteristics of tumors and may be utilized to detect immuno-hot tumors in gastric cancer.

Kinase Inhibitor Pulldown Assay Identifies a Chemotherapy Response Signature in Triple-negative Breast Cancer Based on Purine-binding Proteins

Triple-negative breast cancer (TNBC) constitutes 10%-15% of all breast tumors. The current standard of care is multiagent chemotherapy, which is effective in only a subset of patients. The original objective of this study was to deploy a mass spectrometry (MS)-based kinase inhibitor pulldown assay (KIPA) to identify kinases elevated in non-pCR (pathologic complete response) cases for therapeutic targeting. Frozen optimal cutting temperature compound-embedded core needle biopsies were obtained from 43 patients with TNBC before docetaxel- and carboplatin-based neoadjuvant chemotherapy. KIPA was applied to the native tumor lysates that were extracted from samples with high tumor content. Seven percent of all identified proteins were kinases, and none were significantly associated with lack of pCR. However, among a large population of "off-target" purine-binding proteins (PBP) identified, seven were enriched in pCR-associated samples (P < 0.01). In orthogonal mRNA-based TNBC datasets, this seven-gene "PBP signature" was associated with chemotherapy sensitivity and favorable clinical outcomes. Functional annotation demonstrated IFN gamma response, nuclear import of DNA repair proteins, and cell death associations. Comparisons with standard tandem mass tagged-based discovery proteomics performed on the same samples demonstrated that KIPA-nominated pCR biomarkers were unique to the platform. KIPA is a novel biomarker discovery tool with unexpected utility for the identification of PBPs related to cytotoxic drug response. The PBP signature has the potential to contribute to clinical trials designed to either escalate or de-escalate therapy based on pCR probability.

Significance

The identification of pretreatment predictive biomarkers for pCR in response to neoadjuvant chemotherapy would advance precision treatment for TNBC. To complement standard proteogenomic discovery profiling, a KIPA was deployed and unexpectedly identified a seven-member non-kinase PBP pCR-associated signature. Individual members served diverse pathways including IFN gamma response, nuclear import of DNA repair proteins, and cell death.

A senescence-related signature for predicting the prognosis of breast cancer: A bioinformatics analysis

Breast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. Current gene signatures for prognostic prediction are limited to specific subtypes of breast cancer. Cellular senescence is a state of irreversible cell cycle arrest that affects various physiological and pathological processes. This study aimed to develop and validate a senescence-related signature for predicting the prognosis of breast cancer patients. We retrieved 744 senescence-associated genes from the SeneQuest database and analyzed their expression profiles in 2 large datasets of breast cancer patients: The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We used univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis to derive a 29-gene senescence-related risk signature. The risk signature was significantly associated with disease-specific survival (DSS), clinical characteristics, molecular subtypes, and immune checkpoint genes expressions in both datasets. The risk signature also stratified high-risk and low-risk patients within the same clinical stage and molecular subtype. The risk signature was an independent prognostic factor for breast cancer patients. The senescence-related signature may be a useful biomarker for predicting prognosis and immunotherapy response of breast cancer patients. The risk signature may also guide adjuvant chemotherapy decisions, especially in hormone receptor positive (HR+) and human epidermal growth factor receptor type 2 (HER2)- subtypes.

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.

Immune-related gene index predicts metastasis for prostate cancer patients undergoing radical radiotherapy

In this study, we established a novel immunologic gene prognostic index (IGPI) to predict metastasis and provided new insights into tumor immune microenvironment (TIME) for PCa patients receiving radical radiotherapy. GBP2 and IGF1 were independent factors associated with metastasis-free survival. IGPI score was calculated based on GBP2 and IGF1 and this score was an independent risk factor for PCa patients undergoing radical radiotherapy. Patients with higher IGPI score were at higher risk of metastasis and biochemical recurrence, which were externally validated in the TCGA database and other GEO datasets. IGPI score had demonstrated moderate diagnostic ability of radiation resistance (AUC: 0.889). This score increased with the augment of Gleason score and T stage, as well as biochemical recurrence. Using EPIC, ESTIMATE and immunophenoscore (IPS) algorithms, cancer associated fibroblasts (CAFs), macrophages, stromal score, and estimate score were significantly higher in patients with metastasis group compared to their counterpart. Besides, for CAFs, macrophages, stromal score, and estimate score, patients with higher scores were at higher risk of metastasis, and the HRs were 3.65, 4.01, 4.27, and 3.78, respectively. IGPI score was highly positively associated with stromal score (coefficient: 0.39), immune score (coefficient: 0.43), estimate score (coefficient: 0.45), CAFs (coefficient: 0.42) and macrophages (coefficient: 0.42), while showing the opposite relationship with tumor purity (coefficient: - 0.45). In conclusion, we found that IGPI based on GBP2 and IGF1 might serve as a biomarker predicting metastasis for PCa patients. Besides, the current data further highlight the importance of CAFs in the metastatic process of PCa.

Guanine nucleotide-binding protein 2, GNBP2, accelerates the progression of clear cell renal cell carcinoma via regulation of STAT3 signaling transduction pathway

BACKGROUND

Guanine nucleotide-binding protein 2 (GNBP2) is a GTPase that has critical roles in host immunity and some types of cancer, but its function in clear cell renal cell carcinoma (ccRCC) is not fully understood.

OBJECTIVE

This work explored the role of GNBP2 in ccRCC progression and the underlying molecular mechanism.

METHODS

Two public human cancer databases TNMplot and TISIDB were employed to analyze the expression pattern of GNBP2 during ccRCC progression and the correlation between GNBP2 expression and clinical features of ccRCC patients. GNBP2 functions in ccRCC cells were determined by EdU staining, flow cytometry, scratch wound assay, transwell assay, and xenograft model. Gene expression was evaluated using qPCR, Western blot, immunofluorescence staining, and immunohistochemical staining.

RESULTS

GNBP2 expression was significantly elevated in ccRCC tissues and increased gradually with the increasing tumor grades. Patients with higher GNBP2 expression had shorter overall survival times. Knockdown of GNBP2 suppressed tumor cell proliferation and cell cycle progression and reduced the capability of migration and invasion, while GNBP2 overexpression exhibited protumor effects. GNBP2 silencing by RNA interference significantly inhibited the tumor growth of tumor-bearing nude mice and decreased the proliferation marker Ki67. Mechanistically, GNBP2 downregulation suppressed the STAT3 signaling transduction, as it reduced the phosphorylation of STAT3 and modulated the expression of the target genes, including c-Myc, MMP2, N-cadherin, and E-cadherin.

CONCLUSION

These findings reveal that GNBP2 promotes ccRCC progression by regulating STAT3 signaling transduction, indicating that GNBP2 might be a promising molecular target for ccRCC therapy.

A CD8+ T cell-associated immune gene panel for prediction of the prognosis and immunotherapeutic effect of melanoma

Background

Skin cutaneous melanoma (SKCM) is the most frequently encountered tumor of the skin. Immunotherapy has opened a new horizon in melanoma treatment. We aimed to construct a CD8⁺ T cell-associated immune gene prognostic model (CDIGPM) for SKCM and unravel the immunologic features and the benefits of immunotherapy in CDIGPM-defined SKCM groups.

Method

Single-cell SKCM transcriptomes were utilized in conjunction with immune genes for the screening of CD8⁺ T cell-associated immune genes (CDIGs) for succeeding assessment. Thereafter, through protein-protein interaction (PPI) networks analysis, univariate COX analysis, and multivariate Cox analysis, six genes (MX1, RSAD2, IRF2, GBP2, IFITM1, and OAS2) were identified to construct a CDIGPM. We detected cell proliferation of SKCM cells transfected with IRF2 siRNA. Then, we analyzed the immunologic features and the benefits of immunotherapy in CDIGPM-defined groups.

Results

The overall survival (OS) was much better in low-CDIGPM group versus high CDIGPM group in TCGA dataset and GSE65904 dataset. On the whole, the results unfolded that a low CDIGPM showed relevance to immune response-correlated pathways, high expressions of CTLA4 and PD-L1, a high infiltration rate of CD8⁺ T cells, and more benefits from immunotherapy.

Conclusion

CDIGPM is an good model to predict the prognosis, the potential immune escape from immunotherapy for SKCM, and define immunologic and molecular features.

Roles of mitochondrial fusion and fission in breast cancer progression: a systematic review

Background

Mitochondria play critical roles in cellular physiological activity as cellular organelles. Under extracellular stimulation, mitochondria undergo constant fusion and fission to meet different cellular demands. Mitochondrial dynamics, which are involved in mitochondrial fusion and fission, are regulated by specialized proteins and lipids, and their dysregulation causes human diseases, such as cancer. The advanced literature about the crucial role of mitochondrial dynamics in breast cancer is performed.

Methods

All related studies were systematically searched through online databases (PubMed, Web of Science, and EMBASE) using keywords (e.g., breast cancer, mitochondrial, fission, and fusion), and these studies were then screened through the preset inclusion and exclusion criteria.

Results

Eligible studies (n = 19) were evaluated and discussed in the systematic review. These advanced studies established the roles of mitochondrial fission and fusion of breast cancer in the metabolism, proliferation, survival, and metastasis. Importantly, the manipulating of mitochondrial dynamic is significant for the progresses of breast cancer.

Conclusion

Understanding the mechanisms underlying mitochondrial fission and fusion during tumorigenesis is important for improving breast cancer treatments.

GBP2 acts as a member of the interferon signalling pathway in lupus nephritis

Lupus nephritis (LN) is a common and serious clinical manifestation of systemic lupus erythematosus. However, the pathogenesis of LN is not fully understood. The currently available treatments do not cure the disease and appear to have a variety of side effects in the long term. The purpose of this study was to search for key molecules involved in the LN immune response through bioinformatics techniques to provide a reference for LN-specific targeted therapy. The GSE112943 dataset was downloaded from the Gene Expression Omnibus database, and 20 of the samples were selected for analysis. In total, 2330 differentially expressed genes were screened. These genes were intersected with a list of immune genes obtained from the IMMPORT immune database to obtain 128 differentially expressed immune-related genes. Enrichment analysis showed that most of these genes were enriched in the interferon signalling pathway. Gene set enrichment analysis revealed that the sample was significantly enriched for expression of the interferon signalling pathway. Further analysis of the core gene cluster showed that nine genes, GBP2, VCAM1, ADAR, IFITM1, BST2, MX2, IRF5, OAS1 and TRIM22, were involved in the interferon signalling pathway. According to our analysis, the guanylate binding protein 2 (GBP2), interferon regulatory factor 5 and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes are involved in three interferon signalling pathways. At present, we do not know whether GBP2 is associated with LN. Therefore, this study focused on the relationship between GBP2 and LN pathogenesis. We speculate that GBP2 may play a role in the pathogenesis of LN as a member of the interferon signalling pathway. Further immunohistochemical results showed that the expression of GBP2 was increased in the renal tissues of LN patients compared with the control group, confirming this conjecture. In conclusion, GBP2 is a member of the interferon signalling pathway that may have implications for the pathogenesis of LN and serves as a potential biomarker for LN.

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.

Whole-genome CRISPR screening identifies genetic manipulations to reduce immune rejection of stem cell-derived islets

Human embryonic stem cells (hESCs) provide opportunities for cell replacement therapy of insulin-dependent diabetes. Therapeutic quantities of human stem cell-derived islets (SC-islets) can be produced by directed differentiation. However, preventing allo-rejection and recurring autoimmunity, without the use of encapsulation or systemic immunosuppressants, remains a challenge. An attractive approach is to transplant SC-islets, genetically modified to reduce the impact of immune rejection. To determine the underlying forces that drive immunogenicity of SC-islets in inflammatory environments, we performed single-cell RNA sequencing (scRNA-seq) and whole-genome CRISPR screen of SC-islets under immune interaction with allogeneic peripheral blood mononuclear cells (PBMCs). Data analysis points to “alarmed” populations of SC-islets that upregulate genes in the interferon (IFN) pathway. The CRISPR screen in vivo confirms that targeting IFNγ-induced mediators has beneficial effects on SC-islet survival under immune attack. Manipulating the IFN response by depleting chemokine ligand 10 (CXCL10) in SC-islet grafts confers improved survival against allo-rejection compared with wild-type grafts in humanized mice. These results offer insights into the nature of immune destruction of SC-islets during allogeneic responses and provide targets for gene editing.

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IFN pathway induction sets the fate of SC-islets under allogeneic immune challenge

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“Alarm” genes drive immunogenicity of SC-islets

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Genetically modified SC-islets were generated and evaluated for hypo-immunogenicity

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CXCL10 depletion can reduce immune activation and SC-islet graft rejection

GBP3 promotes glioblastoma resistance to temozolomide by enhancing DNA damage repair

Glioblastoma is the most common malignant brain cancer with dismal survival and prognosis. Temozolomide (TMZ) is a first-line chemotherapeutic agent for glioblastoma, but the emergence of drug resistance limits its anti-tumor activity. We previously discovered that the interferon inducible guanylate binding protein 3 (GBP3) is highly elevated and promotes tumorigenicity of glioblastoma. Here, we show that TMZ treatment significantly upregulates the expression of GBP3 and stimulator of interferon genes (STING), both of which increase TMZ-induced DNA damage repair and reduce cell apoptosis of glioblastoma cells. Mechanistically, relying on its N-terminal GTPase domain, GBP3 physically interacts with STING to stabilize STING protein levels, which in turn induces expression of p62 (Sequestosome 1), nuclear factor erythroid 2 like 2 (NFE2L2, NRF2), and O6-methlyguanine-DNA-methyltransferase (MGMT), leading to the resistance to TMZ treatment. Reducing GBP3 levels by RNA interference in glioblastoma cells markedly increases the sensitivity to TMZ treatment in vitro and in murine glioblastoma models. Clinically, GBP3 expression is high and positively correlated with STING, NRF2, p62, and MGMT expression in human glioblastoma tumors, and is associated with poor outcomes. These findings provide novel insight into TMZ resistance and suggest that GBP3 may represent a novel potential target for the treatment of glioblastoma.

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.

GBP2 facilitates the progression of glioma via regulation of KIF22/EGFR signaling

Identifying the mechanism of glioma progression is critical for diagnosis and treatment. Although studies have shown that guanylate-binding protein 2(GBP2) has critical roles in various cancers, its function in glioma is unclear. In this work, we demonstrate that GBP2 has high expression levels in glioma tissues. In glioma cells, depletion of GBP2 impairs proliferation and migration, whereas overexpression of GBP2 enhances proliferation and migration. Regarding the mechanism, we clarify that epidermal growth factor receptor (EGFR) signaling is regulated by GBP2, and also demonstrate that GBP2 interacts directly with kinesin family member 22(KIF22) and regulates glioma progression through KIF22/EGFR signaling in vitro and in vivo. Therefore, our study provides new insight into glioma progression and paves the way for advances in glioma treatment.

Subtyping of microsatellite stability colorectal cancer reveals guanylate binding protein 2 (GBP2) as a potential immunotherapeutic target

Backgrounds

Proficient-mismatch-repair or microsatellite stability (pMMR/MSS) colorectal cancer (CRC) has limited efficacy for immune checkpoint blockade (ICB) therapy and its underlying mechanism remains unclear. Guanylate binding protein 2 (GBP2) is a member of the GTPase family and is crucial to host immunity against pathogens. However, the correlations between GBP2 and immunosurveillance and immunotherapy for pMMR/MSS CRC have not been reported.

Methods

Unsupervised clustering was employed to classify immune class and non-immune class in 1424 pMMR/MSS patients from six independent public datasets. This binary classification was validated using immune cells or response related signatures. The correlation between GBP2 and immune microenvironment was explored using well-established biological algorithms, multiplex immunohistochemistry (mIHC), in vitro and in vivo experiments.

Results

We classified 1424 pMMR/MSS CRC patients into two classes, ‘immune’ and ‘non-immune’, and GBP2 was identified as a gene of interest. We found that lower GBP2 expression was correlated with poor prognosis and metastasis. GBP2 expression was also upregulated in the immune class and highly associated with interferon-γ (IFN-γ) signaling pathway and CD8 +T cell infiltration using gene set enrichment analysis, gene ontology analysis, single-cell sequencing and mIHC. Moreover, reduced GBP2 expression inhibited the antigen processing and presentation machinery and CXCL10/11 expression in MSS CRC cells on IFN-γ stimulation. A Transwell assay revealed that deletion of GBP2 in murine MSS CRC cells reduced CD8 +T cell migration. Mechanistically, GBP2 promoted signal transducer and transcription activator 1 (STAT1) phosphorylation by competing with SHP1 for binding to STAT1 in MSS CRC cells. Finally, an unsupervised subclass mapping (SubMap) algorithm showed that pMMR/MSS patients with high GBP2 expression may correlate with a favorable response to anti-PD-1 therapy. We further confirmed that GBP2 knockout reduced CD8 +T cell infiltration and blunted the efficacy of PD-1 blockade in tumor-bearing mice.

Conclusions

Our study reveals that pMMR/MSS CRC is immunogenically heterogeneous and that GBP2 is a promising target for combinatorial therapy with ICB.

Adipogenic Transdifferentiation and Regulatory Factors Promote the Progression and the Immunotherapy Response of Renal Cell Carcinoma: Insights From Integrative Analysis

Background

Adipogenic transdifferentiation was an important carcinogenic factor in various tumors, while studies on its role in clear cell renal cell carcinoma (ccRCC) were still relatively few. This study aimed to investigate its prognostic value and mechanism of action in ccRCC.

Methods

Gene expression profiles and clinical data of ccRCC patients were obtained from The Cancer Genome Atlas database. Nonnegative matrix factorization was used for clustering. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to analyze the pathways and biological process activities. single-sample GSEA (ssGSEA) was utilized to quantify the relative abundance of each immune cell. Tumor Immune Estimation Resource (TIMER) was used to evaluate the proportion of various immune infiltrating cells across diverse cancer types. Real-Time PCR was performed to examine the gene expression. R software was utilized to analyze the expression and prognostic role of genes in ccRCC.

Results

A total of 49 adipose-related genes (ARGs) were screened for differential expression between normal and ccRCC tissues. Based on differentially expressed ARGs, patients with ccRCC were divided into two adipose subtypes with different clinical, molecular, and pathway characteristics. Patients in cluster A exhibited more advanced pathological stages, higher expressions of RARRES2 and immune checkpoint genes, higher immune infiltration scores, and less nutrient metabolism pathways. Adipose differentiation index (ADI) was constructed according to the above ARGs and survival data, and its robustness and accuracy was validated in different cohorts. In addition, it was found that the expression of ARGs was associated with immune cell infiltration and immune checkpoint in ccRCC, among which GBP2 was thought to be the most relevant gene to the tumor immune microenvironment and play a potential role in carcinogenesis and invasion of tumor cells.

Conclusion

Our analysis revealed the consistency of higher adipogenic transdifferentiation of tumor cells with worse clinical outcomes in ccRCC. The 16-mRNA signature could predict the prognosis of ccRCC patients with high accuracy. ARGs such as GBP2 might shed light on the development of novel biomarkers and immunotherapies of ccRCC.

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.

An Immunosenescence-Related Gene Signature to Evaluate the Prognosis, Immunotherapeutic Response, and Cisplatin Sensitivity of Bladder Cancer

Immunosenescence refers to the immune system undergoing a series of degenerative changes with advancing age and is tightly associated with the initiation and progression of cancers. However, the immunosenescence-related genes as critical biomarkers for bladder cancer (BLCA) have not been systematically analyzed. We retrieved the immunosenescence-related genes from the public database and verified their association with hallmarks of immunosenescence based on The Cancer Genome Atlas (TCGA) cohort. Through gene pairing, Lasso, and univariate Cox regression, an 8-gene pair model was constructed to evaluate the overall survival of BLCA, which was then validated in the training cohort (P < 0.001, n = 396), two external validation cohorts (P < 0.05, n = 165; P < 0.001, n = 224), and local samples (P < 0.05, n = 10). We also downloaded the clinical information and gene expression matrices of other 32 different cancers from TCGA. The established model showed significant predictive value for the prognosis in 15 cancers (P < 0.05). The risk model could also serve as a promising predictor for immunotherapeutic response, which has been verified by the TIDE algorithm (P < 0.05), IMvigor210 dataset (P < 0.01, n = 298), and other two datasets correlated with immunotherapy (P < 0.05, n = 56; P = 0.17, n = 27). The TCGA dataset, in vitro cell experiments, and pan-cancer analysis displayed that the gene signature was associated with cisplatin sensitivity (P < 0.05). Overall, we proposed a novel immunosenescence-related gene signature to predict prognosis, immunotherapeutic response, and cisplatin sensitivity of BLCA, which were validated in different independent cohorts, local samples, and pan-cancer analyses.

What genes are differentially expressed in individuals with schizophrenia? A systematic review

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Prognostic model based on six PD-1 expression and immune infiltration-associated genes predicts survival in breast cancer

Background

The prognosis of breast cancer (BC) was associated with the expression of programmed cell death-1 (PD-1).

Methods

BC-related expression and clinical data were downloaded from TCGA database. PD-1 expression with overall survival and clinical factors were investigated. Gene set variation analysis (GSVA) and weighted gene correlation network analysis were performed to investigate the PD-1 expression-associated KEGG pathways and genes, respectively. Immune infiltration was analyzed using the ssGSEA algorithm and DAVID, respectively. Univariate and multivariable Cox and LASSO regression analyses were performed to select prognostic genes for modeling.

Results

High PD-1 expression was related to prolonged survival time (P = 0.014). PD-1 expression status showed correlations with age, race, and pathological subtype. ER- and PR-negative patients exhibited high PD-1 expression. The GSVA revealed that high PD-1 expression was associated with various immune-associated pathways, such as T cell/B cell receptor signaling pathway or natural killer cell-mediated cytotoxicity. The patients in the high-immune infiltration group exhibited significantly higher PD-1 expression levels. In summary, 397 genes associated with both immune infiltration and PD-1 expression were screened. Univariate analysis and LASSO regression model identified the six most valuable prognostic genes, namely IRC3, GBP2, IGJ, KLHDC7B, KLRB1, and RAC2. The prognostic model could predict survival for BC patients.

Conclusion

High PD-1 expression was associated with high-immune infiltration in BC patients. Genes closely associated with PD-1, immune infiltration and survival prognosis were screened to predict prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12282-022-01344-2.

Guanylate-Binding Protein 1 as a Potential Predictor of Immunotherapy: A Pan-Cancer Analysis

Background: Mainstream application of cancer immunotherapy is hampered by the low response rate of most cancer patients. A novel immunotherapeutic target or a biomarker predicting response to immunotherapy needs to be developed. Guanylate-binding protein 1 (GBP1) is an interferon (IFN)-inducible guanosine triphosphatases (GTPases) involving inflammation and infection. However, the immunological effects of GBP1 in pan-cancer patients are still obscure.

Methods: Using large-scale public data, we delineated the landscape of GBP1 across 33 cancer types. The correlation between GBP1 expression or mutation and immune cell infiltration was estimated by ESTIMATE, TIMER, xCell, and quanTIseq algorithms. GBP1-related genes and proteins were subjected to function enrichment analysis. Clustering analysis explored the relationship between GBP1 expression and anti-tumor immune phenotypes. We assessed the patient’s response to immunotherapy using the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). Furthermore, we validated the predictive power of GBP1 expression in four independent immunotherapy cohorts.

Results: GBP1 was differentially expressed in tumors and normal tissues in multiple cancer types. Distinct correlations existed between GBP1 expression and prognosis in cancer patients. GBP1 expression and mutation were positively associated with immune cell infiltration. Function enrichment analysis showed that GBP1-related genes were enriched in immune-related pathways. Positive correlations were also observed between GBP1 expression and the expression of immune checkpoints, as well as tumor mutation burden (TMB). Pan-cancer patients with higher GBP1 expression were more inclined to display “hot” anti-tumor immune phenotypes and had lower TIDE scores and higher immunophenoscore, suggesting that these patients had better responses to immunotherapy. Patients with higher GBP1 expression exhibited improved overall survival and clinical benefits in immunotherapy cohorts, including the Gide et al. cohort [area under the curve (AUC): 0.813], the IMvigor210 cohort (AUC: 0.607), the Lauss et al. cohort (AUC: 0.740), and the Kim et al. cohort (AUC: 0.793).

Conclusion: This study provides comprehensive insights into the role of GBP1 in a pan-cancer manner. We identify GBP1 expression as a predictive biomarker for immunotherapy, potentially enabling more precise and personalized immunotherapeutic strategies in the future.

The Prognostic and Immunological Value of Guanylate-Binding Proteins in Lower-Grade Glioma: Potential Markers or Not?

Seven guanylate-binding proteins (GBPs, GBP1–7), identified as a subfamily of interferon-γ-induced guanosine triphosphate hydrolases (GTPases), has been reported to be closely associated with tumor progression, metastasis, and prognosis of cancer patients in recent years. However, the expression patterns, prognostic value, immune infiltration relevance, and biological functions of GBPs in lower-grade glioma (LGG) remain elusive. In this study, by analysis and verification through multiple public data platforms, we found that GBP1, 2, 3, 4 were significantly upregulated in LGG tissues vs normal brain tissue. Analysis based on the Cox proportional hazard ratio and Kaplan–Meier plots demonstrated that the high expressions of GBP 1, 2, 3, 4 were significantly correlated with the poor prognosis of LGG patients. Correlation analysis of clinical parameters of LGG patients indicated that the expressions of GBP 1, 2, 3, 4 were significantly associated with the histological subtype and tumor histological grade of LGG. Furthermore, the correlation analysis of immune infiltration showed that the expressions of GBP1, 2, 3, 4 were significantly and positively correlated with the level of tumor immune-infiltrating cells. In particular, GBP1, 2, 3, 4 expressions were strongly correlated with the infiltration levels of monocyte, TAM, and M1/M2 macrophage, revealing their potential to regulate the polarity of macrophages. Finally, we used the GSEA method to explore the signaling pathways potentially regulated by GBP1, 2, 3, 4 and found that they were all closely associated with immune-related signaling pathways. Collectively, these findings suggested that GBP1, 2, 3, 4 were potent biomarkers to determine the prognosis and immune cell infiltration of LGG patients.

The Large GTPase, GBP-2, Regulates Rho Family GTPases to Inhibit Migration and Invadosome Formation in Breast Cancer Cells

Simple Summary

Too many women still die of breast cancer each year. Those breast cancers that kill are those with cells that have migrated away from the primary tumor in the breast and established new tumors at other sites in the body. These tumors are not reached when the original tumor in the breast is removed. This study was designed to determine why some breast cancers move away from their primary tumor and others do not. We have identified a protein that inhibits this movement. Understanding this finding may provide us with ways to inhibit tumor cell movement in patients.

Abstract

Breast cancer is the most common cancer in women. Despite advances in early detection and treatment, it is predicted that over 43,000 women will die of breast cancer in 2021. To lower this number, more information about the molecular players in breast cancer are needed. Guanylate-Binding Protein-2 has been correlated with better prognosis in breast cancer. In this study, we asked if the expression of GBP-2 in breast cancer merely provided a biomarker for improved prognosis or whether it actually contributed to improving outcome. To answer this, the 4T1 model of murine breast cancer was used. 4T1 cells themselves are highly aggressive and highly metastatic, while 67NR cells, isolated from the same tumor, do not leave the primary site. The expression of GBP-2 was examined in the two cell lines and found to be inversely correlated with aggressiveness/metastasis. Proliferation, migration, and invadosome formation were analyzed after altering the expression levels of GBP-2. Our experiments show that GBP-2 does not alter the proliferation of these cells but inhibits migration and invadosome formation downstream of regulation of Rho GTPases. Together these data demonstrate that GBP-2 is responsible for cell autonomous activities that make breast cancer cells less aggressive.

Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes

Background

Bladder cancer is the tenth most common cancer worldwide. Valuable biomarkers in the field of diagnostic bladder cancer are urgently required.

Method

Here, the gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA), GSE13507, GSE32894, and Mariathasan et al. Five prognostic genes were identified by the univariate, robust, and multivariate Cox's regression and were used to develop a prognosis-related model. The Kaplan-Meier survival curves and receiver operating characteristics were used to evaluate the model's effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. Cancer Therapeutics Response Portal (CTRP) and PRISM datasets were used to identify drugs with high sensitivity. Subsequently, using the bladder cancer (BLCA) cell lines, the role of TNFRSF14 was determined by Western blotting, cell proliferation assay, and 5-ethynyl-20-deoxyuridine assay.

Results

GSDMB, CLEC2D, APOL2, TNFRSF14, and GBP2 were selected as prognostic genes in bladder cancer patients. The model's irreplaceable reliability was validated by the training and validation cohorts. CD8+ T cells were highly infiltrated in the high-TNFRSF14-expression group, and M2 macrophages were the opposite. Higher expression of TNFRSF14 was associated with higher expression levels of LCK, interferon, MHC-I, and MHC-II, while risk score was the opposite. Many compounds with higher sensitivity for treating bladder cancer patients in the low-TNFRSF14-expression group were identified, with obatoclax being a potential drug most likely to treat patients in the low-TNFRSF14-expression group. Finally, the proliferation of BLCA cell lines was increased in the TNFRSF14-reduced group, and the differential expression was identified. TNFRSF14 plays a role in bladder cancer progression through the Wnt/β-catenin-dependent pathway. TNFRSF14 is a potential protective biomarker involved in cell proliferation in BLCA.

Conclusion

We conducted a study to establish a 5-gene score model, providing reliable prediction for the outcome of bladder cancer patients and therapeutic drugs to individualize therapy. Our findings provide a signature that might help determine the optimal treatment for individual patients with bladder cancer.

Clinical Significance and the Role of Guanylate-Binding Protein 5 in Oral Squamous Cell Carcinoma

Guanylate binding protein 5 (GBP5) is the interferon (IFN)-inducible subfamily of guanosine triphosphatases (GTPases) and is involved in pathogen defense. However, the role played by GBP5 in cancer development, especially in oral squamous cell carcinoma (OSCC), is still unknown. Herein, next-generation sequencing analysis showed that the gene expression levels of GBP5 were significantly higher in OSCC tissues compared with those found in corresponding tumor adjacent normal tissues (CTAN) from two pairs of OSCC patients. Higher gene expression levels of GBP5 were also found in tumor tissues of 23 buccal mucosal squamous cell carcinoma (BMSCC)/14 tongue squamous cell carcinoma (TSCC) patients and 30 oral cancer patients from The Cancer Genome Atlas (TCGA) database compared with those in CTAN tissues. Immunohistochemical results showed that protein expression levels of GBP5 were also higher in the tumor tissues of 353 OSCC patients including 117 BMSCC, 187 TSCC, and 49 lip squamous cell carcinoma patients. Moreover, TCGA database analysis indicated that high gene expression levels of GBP5 were associated with poor overall survival in oral cancer patients with moderate/poor cell differentiation, and associated with poor disease-free survival in oral cancer patients with moderate/poor cell differentiation and lymph node metastasis. Furthermore, GBP5-knockdowned cells exhibited decreased cell growth, arrest at G1 phase, and decreased invasion/migration. The gene expression of markers for epithelial-mesenchymal transition and cancer stemness was also reduced in GBP5-silenced oral cancer cells. Taken together, GBP5 might be a potential biomarker and therapeutic target for OSCC patients, especially for those with poor cell differentiation and lymph node metastasis.

TGF-β Increases MFGE8 Production in Myeloid-Derived Suppressor Cells to Promote B16F10 Melanoma Metastasis

There is growing evidence that myeloid-derived suppressor cells (MDSCs) are directly involved in all stages leading to metastasis. Many mechanisms for this effect have been proposed, but mechanisms of coregulation between tumor cells and MDSCs remain poorly understood. In this study, we demonstrate that MDSCs are a source of milk fat globule-epidermal growth factor (EGF) factor 8 (MFGE8), which is known to be involved in tumor metastasis. Interestingly, TGF-β, an abundant cytokine in the tumor microenvironment (TME), increased MFGE8 production by MDSCs. In addition, co-culturing MDSCs with B16F10 melanoma cells increased B16F10 cell migration, while MFGE8 neutralization decreased their migration. Taken together, these findings suggest that MFGE8 is an important effector molecule through which MDSCs promote tumor metastasis, and the TME positively regulates MFGE8 production by MDSCs through TGF-β.

Guanylate-binding proteins induce apoptosis of leukemia cells by regulating MCL-1 and BAK

Interferon-inducible guanylate-binding proteins (GBPs) are well-known for mediating host-defense mechanisms against cellular pathogens. Emerging evidence suggests that GBPs are also implicated in tumorigenesis; however, their underlying molecular mechanism is still unknown. In this study, we identified that GBP1 and GBP2 interact with MCL-1, the key prosurvival member of the BCL-2 family, via its BH3 domain. GBPs induce caspase-dependent apoptosis in chronic myeloid leukemia (CML) and acute myeloid leukemia (AML) cells, where the proapoptotic BCL-2 member, BAK, is an indispensable mediator. In particular, GBP2 completely inhibited the MCL-1-mediated promotion of the survival of CML cells through competitive inhibition, resulting in BAK liberation from MCL-1. Concurrently, GBP2 dramatically upregulates BAK expression via its inhibition of the PI3K/AKT pathway. Moreover, paclitaxel upregulates GBP2 expression, and paclitaxel-induced apoptotic activity was distinctively compromised by knockout of GBP2 in CML cells. Bioinformatics analyses of leukemia databases revealed that transcripts of GBPs were generally downregulated in leukemia patients and that GBPs were favorable prognosis markers. Thus, these findings provide molecular evidence of GBPs as apoptosis-inducing proteins of leukemia cells and suggest that GBPs are attractive targets for the development of chemotherapeutics.

GBP2 as a potential prognostic biomarker in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is a disease with atypical symptoms, an unfavorable response to therapy, and a poor outcome. Abnormal guanylate-binding proteins (GBPs) play an important role in the host's defense against viral infection and may be related to carcinogenesis. In this study, we sought to determine the relationship between GBP2 expression and phenotype in patients with PAAD and explored the possible underlying biological mechanism.

Method

We analyzed the expression of GBP2 in PAAD tissues using a multiple gene expression database and a cohort of 42 PAAD patients. We evaluated GBP2's prognostic value using Kaplan-Meier analysis and the Cox regression model. GO and KEGG enrichment analysis, co-expression analysis, and GSEA were performed to illustrate the possible underlying biological mechanism. CIBERSORT and the relative expression of immune checkpoints were used to estimate the relationship between GBP2 expression and tumor immunology.

Result

GBP2 was remarkably overexpressed in PAAD tissue. The overexpression of GBP2 was correlated with an advanced T stage and poor overall survival (OS) and GBP2 expression was an independent risk factor for OS in PAAD patients. Functional analysis demonstrated that positively co-expressed genes of GBP2 were closely associated with pathways in cancer and the NOD-like receptor signaling pathway. Most of the characteristic immune checkpoints, including PDCD1, PDCDL1, CTLA4, CD80, TIGIT, LAG3, IDO2, and VISTA, were significantly expressed in the high-GBP2 expression group compared with the low-GBP2 expression group.

Conclusion

GBP2 acted as a potential prognostic biomarker and was associated with immune infiltration and the expression of immune checkpoints in PAAD.

Identification of Potential Bisphenol A (BPA) Exposure Biomarkers in Ovarian Cancer

Endocrine-disrupting chemicals (EDCs) can exert multiple deleterious effects and have been implicated in carcinogenesis. The xenoestrogen Bisphenol A (BPA) that is found in various consumer products has been involved in the dysregulation of numerous signalling pathways. In this paper, we present the analysis of a set of 94 genes that have been shown to be dysregulated in presence of BPA in ovarian cancer cell lines since we hypothesised that these genes might be of biomarker potential. This study sought to identify biomarkers of disease and biomarkers of disease-associated exposure. In silico analyses took place using gene expression data extracted from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Differential expression was further validated at protein level using immunohistochemistry on an ovarian cancer tissue microarray. We found that 14 out of 94 genes are solely dysregulated in the presence of BPA, while the remaining 80 genes are already dysregulated (p-value < 0.05) in their expression pattern as a consequence of the disease. We also found that seven genes have prognostic power for the overall survival in OC in relation to their expression levels. Out of these seven genes, Keratin 4 (KRT4) appears to be a biomarker of exposure-associated ovarian cancer, whereas Guanylate Binding Protein 5 (GBP5), long intergenic non-protein coding RNA 707 (LINC00707) and Solute Carrier Family 4 Member 11 (SLC4A11) are biomarkers of disease. BPA can exert a plethora of effects that can be tissue- or cancer-specific. Our in silico findings generate a hypothesis around biomarkers of disease and exposure that could potentially inform regulation and policy making.

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.

Correlation of immune infiltration with clinical outcomes in breast cancer patients: The 25-gene prognostic signatures model

PURPOSE

Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.

METHODS

The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.

RESULTS

Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed.

CONCLUSIONS

This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.

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

Blood methylated cell-free DNA (cfDNA) as a minimally invasive cancer biomarker has great importance in cancer management. Guanylate binding protein 2 (GBP2) has been considered as a possible controlling factor in tumor development. GBP2 gene expression and its promoter methylation status in both plasma cfDNA and tumor tissues of ductal carcinoma breast cancer patients were analyzed using SYBR green comparative Real-Time RT-PCR and, Methyl-specific PCR techniques, respectively in order to find a possible cancer-related marker. The results revealed that GBP2 gene expression and promoter methylation were inversely associated. GBP2 was down-regulated in tumors with emphasis on triple negative status, nodal involvement and higher cancer stages (p<0.0001). GBP2 promoter methylation on both cfDNA and tumor tissues were positively correlated and was detected in about 88% of breast cancer patients mostly in (Lymph node positive) LN+ and higher stages. Data provided shreds of evidence that GBP2 promoter methylation in circulating DNA may be considered as a possible effective non-invasive molecular marker in poor prognostic breast cancer patients with the evidence of its relation to disease stage and lymph node metastasis. However further studies need to evaluate the involvement of GBP2 promoter methylation in progression-free survival or overall survival of the patients.

Identification of immune-related genes with prognostic significance in the microenvironment of cutaneous melanoma

Cutaneous melanoma is one of the most aggressive cancers characterized by increasing incidence and mortality. In recent years, the emergence of immunotherapy has greatly raised the survival rate of patients suffering from cutaneous melanoma, yet some sufferers remain to have poor outcomes after treatment mainly due to the tumor microenvironment (TME). In this study, cutaneous melanoma-associated TME was systematically analyzed using the ESTIMATE algorithm based on the gene transcriptome data obtained from the TCGA database. Totally, 471 patients were included and 553 TME-related genes were screened. Afterwards, a 3-gene signature-based model (CLEC4A, GBP4, KIR2DL4) was constructed via univariate Cox, LASSO, and multivariate Cox regression analyses. To validate the validity of this model, ROC analysis was conducted, and the model was further validated to be an independent prognostic biomarker through univariate and multivariate regression analyses. Finally, the three genes in the model were studied by GSEA and GSVA for their biological significance. We found that the three genes could promote cancer immune response predominantly through affecting immune-related pathways such as antigen processing and presentation, and they may help tumor cells in escaping from surveillance of the immune system when their expression levels were decreased. Additionally, we as well discovered that the expression of the three genes was significantly and positively correlated with the infiltration of related immune cells, but negatively associated with tumor purity. Overall, this study comprehensively analyzed the TME of cutaneous melanoma, identified related biomarkers, and discovered their association with immune system.

Development of an IFNγ response-related signature for predicting the survival of cutaneous melanoma

Background

The tumor microenvironment (TME) plays a critical role in tumorigenesis, development, and therapeutic efficacy. Major advances have been achieved in the treatment of various cancers through immunotherapy. Nevertheless, only a minority of patients have positive responses to immunotherapy, which is partly due to conditions of the immunosuppressive microenvironment. Therefore, it is essential to identify prognostic biomarkers that reflect heterogeneous landscapes of the TME.

Methods and materials

Based upon the ESTIMATE algorithm, we evaluated the infiltrating levels of immune and stromal components derived from patients afflicted by various types of cancer from The Cancer Genome Atlas database (TCGA). According to respective patient immune and stromal scores, we categorized cases into high‐ and low‐scoring subgroups for each cancer type to explore associations between TME and patient prognosis. Gene Set Enrichment Analyses (GSEA) were conducted and genes enriched in IFNγ response signaling pathway were selected to facilitate establishment of a risk model for predicting overall survival (OS). Furthermore, we investigated the associations between the prognostic signature and tumor immune infiltration landscape by using CIBERSORT algorithm and TIMER database.

Results

Among the cancers assessed, the immune scores for skin cutaneous melanoma (SKCM) were the most significantly correlated with patients' survival time (P < .0001). We identified and validated a five‐IFNγ response‐related gene signature (UBE2L6, PARP14, IFIH1, IRF2, and GBP4), which was closely correlated with the prognosis for SKCM afflicted patients. Multivariate Cox regression analysis indicated that this risk model was an independent prognostic factor for SKCM. Tumor‐infiltrating lymphocytes and specific immune checkpoint molecules had notably differential levels of expression in high‐ compared to low‐risk samples.

Conclusion

In this study, we established a novel five‐IFNγ response‐related gene signature that provided a better and increasingly comprehensive understanding of tumor immune landscape, and which demonstrated good performance in predicting outcomes for patients afflicted by SKCM.

GBP2 enhances glioblastoma invasion through Stat3/fibronectin pathway

Guanylate-binding protein 2 (GBP2) is an interferon-inducible large GTPase which is crucial to the protective immunity against microorganisms. However, its biological function in cancer remains largely unknown. Glioblastoma multiforme (GBM) is the most common and deadly brain tumor in adults. Here we show that GBP2 expression is highly elevated in GBM tumor and cell lines, particularly in those of the mesenchymal subtype. High GBP2 expression is associated with poor prognosis. GBP2 overexpression significantly promotes GBM cell migration and invasion in vitro, and GBP2 silencing by RNA interference exhibits opposite effects. We further show that fibronectin (FN1) is dramatically induced by GBP2 expression at both mRNA and protein levels, and FN1 is essential for GBP2-promoted GBM invasiveness. Inhibition of Stat3 pathway prevents GBP2-promoted FN1 induction and cell invasion. Consistently, GBP2 dramatically promotes GBM tumor growth and invasion in mice and significantly reduces the survival time of the mice with tumor. Taken together, these findings establish the role of GBP2/Stat3/FN1 signaling cascade in GBM invasion and suggest GBP2 may serve as a potential therapeutic target for inhibiting GBM invasion.

Comprehensive Analysis of the Expression and Prognosis for GBPs in Head and neck squamous cell carcinoma

Guanylate binding proteins (GBPs) belongs to the interferons (IFNs) induced guanylate-binding protein family (Guanosine triphosphatases, GTPases) consisting of seven homologous members, termed GBP1 to GBP7. We used multidimensional survey ways to explore GBPs expression, regulation, mutations, immune infiltration and functional networks in head and neck squamous cell carcinoma (HNSCC) patient data based on various open databases. The study provides staggered evidence for the significance of GBPs in HNSCC and its potential role as a novel biomarker. Our results showed that over expressions of 7 GBPs members and multivariate analysis suggested that N-stage, high expressions of GBP1 and low expression of GBP6/7 were linked to shorter OS in HNSCC patients. In addition, B cells of immune infiltrates stimulant the prognosis and might have a medical prognostic significance linked to GBPs in HNSCC. We assume that GBPs play a synergistic role in the viral related HNSCC. Our results show that data mining efficiently reveals information about GBPs expression in HNSCC and more importance lays a foundation for further research on the role of GBPs in cancers.

Guanylate-binding protein 6 is a novel biomarker for tumorigenesis and prognosis in tongue squamous cell carcinoma

OBJECTIVES

Guanylate-binding protein 6 (GBP6) is a member of the guanylate-binding protein family, and its role in cancer has not yet been reported. We aimed to investigate the clinical significance of GBP6 in oral squamous cell carcinoma (OSCC).

MATERIALS AND METHODS

Next-generation sequencing was applied for analyzing differential gene expression profiling between corresponding tumor adjacent normal (CTAN) and tumor tissue from two paired OSCC patients. Real-time PCRs (RT-PCRs) were used to investigate the gene expression level of GBP6 of CTAN and tumor tissue samples from 14 TSCC patients. Immunohistochemistry was used to investigate the protein expression level of GBP6 in tumor tissues and paired CTAN tissues from 488 OSCC patients, including 183 buccal mucosa squamous cell carcinoma (BMSCC), 245 tongue squamous cell carcinoma (TSCC), and 60 lip squamous cell carcinoma (LSCC) patients.

RESULTS

Compared with CTAN tissues of OSCC patients, GBP6 is identified as a downregulated gene using the NGS platform, which was confirmed in 14 OSCC patients by RT-PCR. Moreover, protein expression level of GBP6 in tumor tissues was lower than that in CTAN tissues and the low GBP6 expression was correlated with poor cell differentiation/lymph node metastasis in TSCC patients. In addition, TSCC patients with low expression levels of GBP6 had poor disease-specific survival rate.

CONCLUSION

The low expression of GBP6 was associated with tumorigenesis and poor prognosis in OSCC patients, especially in TSCC patients.

CLINICAL RELEVANCE

GBP6 may serve as a novel favorable diagnostic and prognostic biomarker in TSCC patients.

Interferons α and β in cancer: therapeutic opportunities from new insights

Over the past decade, preclinical and clinical research have confirmed the essential role of interferons for effective host immunological responses to malignant cells. Type I interferons (IFNα and IFNβ) directly regulate transcription of >100 downstream genes, which results in a myriad of direct (on cancer cells) and indirect (through immune effector cells and vasculature) effects on the tumour. New insights into endogenous and exogenous activation of type I interferons in the tumour and its microenvironment have given impetus to drug discovery and patient evaluation of interferon-directed strategies. When combined with prior observations or with other effective modalities for cancer treatment, modulation of the interferon system could contribute to further reductions in cancer morbidity and mortality. This Review discusses new interferon-directed therapeutic opportunities, ranging from cyclic dinucleotides to genome methylation inhibitors, angiogenesis inhibitors, chemoradiation, complexes with neoantigen-targeted monoclonal antibodies, combinations with other emerging therapeutic interventions and associations of interferon-stimulated gene expression with patient prognosis - all of which are strategies that have or will soon enter translational clinical evaluation.

Transcriptional profiling of circulating tumor cells in multiple myeloma: a new model to understand disease dissemination

The reason why a few myeloma cells egress from the bone marrow (BM) into peripheral blood (PB) remains unknown. Here, we investigated molecular hallmarks of circulating tumor cells (CTCs) to identify the events leading to myeloma trafficking into the bloodstream. After using next-generation flow to isolate matched CTCs and BM tumor cells from 32 patients, we found high correlation in gene expression at single-cell and bulk levels (r ≥ 0.94, P = 10^-16), with only 55 genes differentially expressed between CTCs and BM tumor cells. CTCs overexpressed genes involved in inflammation, hypoxia, or epithelial-mesenchymal transition, whereas genes related with proliferation were downregulated in CTCs. The cancer stem cell marker CD44 was overexpressed in CTCs, and its knockdown significantly reduced migration of MM cells towards SDF1-α and their adhesion to fibronectin. Approximately half (29/55) of genes differentially expressed in CTCs were prognostic in patients with newly-diagnosed myeloma (n = 553; CoMMpass). In a multivariate analysis including the R-ISS, overexpression of CENPF and LGALS1 was significantly associated with inferior survival. Altogether, these results help understanding the presence of CTCs in PB and suggest that hypoxic BM niches together with a pro-inflammatory microenvironment induce an arrest in proliferation, forcing tumor cells to circulate in PB and seek other BM niches to continue growing.

Guanylate-binding protein-2 inhibits colorectal cancer cell growth and increases the sensitivity to paclitaxel of paclitaxel-resistant colorectal cancer cells by interfering Wnt signaling

Among the GTPase family members, guanylate-binding protein-1 (GBP-1) is the most thoroughly studied member in a plethora of human cancers. GBP-2, on the other hand, remains limitedly studied. We wonder how GBP-2 participates in colorectal carcinoma (CRC) as well as the paclitaxel (PTX)-resistance of CRC. In this study, the authors are determined to dig into the role that GBP-2 plays in the sensitivity of CRC to PTX, therefore, possibly indicating a promising gene therapy target for CRC. Forced expression of GBP-2 gene was done by plasmid transfection. Reverse transcriptase-polymerase chain reaction and immunoblot were conducted to detect the expression of GBP-2 messenger RNA (mRNA) and protein, respectively. Colony foci formation assay, transwell invasion assay, and flow cytofluorometry were done to determine the proliferation, invasion, and apoptosis of PTX-resistant and PTX-sensitive CRC cell lines, respectively. The level of GBP-2 mRNA and protein in PTX-resistant CRC cell lines was significantly lower than in nonresistant cell lines. Forced exogenous expression of GBP-2 in PTX-resistant CRC cell lines resulted in more sensitivity to PTX because of the demonstration of less cell proliferation, invasion, and more apoptosis. Wnt signaling was suppressed when GBP-2 was upregulated by transfection of GBP-2 overexpression plasmids, and Wnt signaling did not affect GBP-2 expression. GBP-2 upregulation could enhance the killing effect of PTX in both PTX-sensitive CRC cells and PTX-resistant CRC cells by suppressing Wnt signaling.

Discovery of core genes in colorectal cancer by weighted gene co-expression network analysis

The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in colorectal cancer (CRC), in order to examine its underlying mechanisms. The raw gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. An online tool, GEO2R, which is based on the limma package, was used to identify differentially expressed genes. The co-expression between lncRNAs and mRNAs was identified utilizing the weighted gene co-expression analysis package of R to construct a coding non-coding (CNC) network. The function of the genes in the CNC network was determined by performing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways enrichment analysis. The interactions among miRNAs, mRNAs and lncRNAs were predicted using Lncbase and mirWalk to construct the competing endogenous RNA (ceRNA) network. The expression of the genes involved in the ceRNA network was further validated in The Cancer Genome Atlas dataset. A total of 3,183 dysregulated mRNAs, 78 dysregulated miRNAs and 2,248 dysregulated lncRNAs were screened in two GEO datasets. Combined with the results of the dysregulated genes, 169 genes were selected to construct the CNC network. 'p53 signaling pathway' and the 'cell cycle' were the most significant enriched pathways in the genes involved in the CNC network. Finally, a validated ceRNA network composed of 2 lncRNAs (MIR22HG and RP11-61I13.3), 5 miRNAs (hsa-miR-765, hsa-miR-198, hsa-miR-125a-3p, hsa-miR-149-3p and hsa-miR-650) and 5 mRNAs (ANK2, BTK, GBP2, PCSK5 and PDK4) was obtained. In conclusion, MIR22HG may regulate PCSK5, BTK and PDK4, and RP11-61I13.3 may regulate the ANK2, GBP2, PCSK5 through sponging miRNAs to act on the progression of CRC, and the potential function of these genes have been revealed. However, the diagnostic and prognostic value of these genes requires further validation.