Distinct prognostic value of mRNA expression of guanylate-binding protein genes in skin cutaneous melanoma

Integrative analysis of the immunological significances of guanylate binding protein family genes in microsatellite stability colorectal cancer

Background

Microsatellite stability (MSS) colorectal cancer (CRC) has poor sensitivity to immunotherapy and its underlying mechanisms are still unclear. Guanylate binding proteins (GBPs) are a family of GTPase involving innate immune responses by providing defense against invading microbes and pathogens. However, the immunological significances of GBPs in MSS CRC remain unknown.

Methods

We utilized bioinformatic tools to comprehensively analysis the expression pattern, clinical relevance, prognostic value, biological function, and immunoregulation effect of distinct GBP members in MSS CRC.

Results

The expression of all seven GBPs in MSS samples are remarkably decreased compared to microsatellite instability-high (MSI-H) samples. Among them, GBP1/2/4/5 are obviously correlated with distant metastasis status. High expression of GBP1/4/5/6 was remarkably related to favorable overall survival (OS) and progression-free survival (PFS) in CRC patients with MSS tumor. Subsequent enrichment analysis revealed that Interferon-gamma (IFN-γ) and NOD-like receptor signaling are the most relevant functions. Besides, the expression patterns of GBPs are remarkably associated with several tumor infiltrated immune cells (e.g. regulatory T cells, CD4+ T cells, and macrophages) and diverse immunoregulatory molecules (e.g. immune checkpoint biomarkers (ICBs) and major histocompatibility complex (MHC) molecules). Moreover, high GBP1/2/4/5 expression predicted better immunotherapy responsiveness in immunotherapy cohorts.

Conclusion

These findings might provide novel insights for the identification of therapeutic targets and potential prognostic biomarkers of GBP family in CRC with MSS samples.

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.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma

Objective

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments.

Methods

Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2.

Results

An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated.

Conclusion

RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

Function and mechanism of GBP1 in the development and progression of cervical cancer

Guanylate binding protein 1 (GBP1) is the most concerned member of the GBP family, which has a series of effects such as anti-infection and anti-angiogenesis. Its role in malignant tumors including cervical cancer is still controversial. We aim to explore the effects of GBP1 on cervical cancer through bioinformatics and related experiments. In this study, we first found that GBP1 was generally expressed in cervical cancer in various online databases and was closely related to immune invasion. Secondly, we used multicolor immunofluorescence technology to verify the expression of GBP1 in cervical cancer tissues and its relationship with immune invasion, and explored its relationship with the prognosis of patients with cervical cancer. Knockdown and overexpression assays of GBP1 in vitro were used to prove GBP1 as a potential oncogene of cervical cancer, and its carcinogenicity was verified by in vivo experiment. In order to explore the potential mechanism of GBP1 in promoting cancer, RNA-seq was performed on GBP1 overexpression and knockdown expression cell lines, and GBP1 knockdown and overexpression were found to be associated with many RNA alternative splicing events, suggesting that GBP1 maybe a RNA binding protein (RBP) which affect the biological characteristics of cervical cancer cells through the alternative splicing pathway. However, the later RNA binding protein immunoprecipitation (RIP) assay proved that GBP1 was not a direct alternative splicing factor, while the co-immunoprecipitation (CoIP)-mass spectroscopy (MS) assay combined with protein protein interaction (PPI) analysis proved that 8 alternative splicing factors including Heterogeneous Nuclear Ribonucleoprotein K (HNRNPK) were interacting proteins of GBP1. Combined with the existing reports and the results of RNA-seq alternative splicing analysis, it is speculated that GBP1 may regulate the alternative splicing of CD44 protein by binding to interacting protein-HNRNPK, and thus play a role in promoting cancer in cervical cancer.

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

Background

The discovery and development of immune checkpoint inhibitors (ICIs) has significantly enhanced the arsenal of immunotherapy treatments available for cancer patients. The identification of biomarkers that are indicative of an individual's sensitivity to treatment with ICIs is useful for screening SCLC patients prior to commencement of any ICIs based immunotherapy. However, the relationship between GBP5 and the prognosis of SCLC immunotherapy is still unclear and requires further study.

Methods

We downloaded two SCLC datasets, namely the George-SCLC and Jiang-SCLC cohorts. We used the TIDE algorithm to predict the efficacy of immunotherapy for SCLC patients. The QuanTIseq, MCPcounter, and EPIC algorithms are used to calculate the proportions of immune cells in SCLC patients. Additionally, we retrospectively collected 35 SCLC samples from the first affiliated hospital of the Hengyang Medical school.

Results

Patients in each cohort were devided into two groups with high (GBP5-High) and low (GBP5-Low) expression of GBP5. In both cohorts, the GBP5-High population had a higher proportion of patients that responded well to immunotherapy (responders) (p < 0.05). In addition, both GBP5-High subgroups had significantly increased cytotoxicity, chemokines, antigen presenting, and TNF family related genes. We also determined that GBP5 was related to high-level infiltration of B cells, CD4+T cells, CD8+T cells and NK cells.

Conclusion

In this study, we found that GBP5 has the potential to be used as a biomarker of ICIs efficacy for SCLC patients. GBP5 is related to the quantity of inflammatory molecules, a high level of immune infiltration, and a highly activated immune response pathway.

Molecular regulatory mechanism of key LncRNAs in subclinical mastitic cows with folic acid supplementation

BACKGROUND

Folic acid is a water-soluble B vitamin (B9), which is closely related to the body's immune and other metabolic pathways. The folic acid synthesized by rumen microbes has been unable to meet the needs of high-yielding dairy cows. The incidence rate of subclinical mastitis in dairy herds worldwide ranged between 25%~65% with no obvious symptoms, but it significantly causes a decrease in lactation and milk quality. Therefore, this study aims at exploring the effects of folic acid supplementation on the expression profile of lncRNAs, exploring the molecular mechanism by which lncRNAs regulate immunity in subclinical mastitic dairy cows.

RESULTS

The analysis identified a total of 4384 lncRNA transcripts. Subsequently, differentially expressed lncRNAs in the comparison of two groups (SF vs. SC, HF vs. HC) were identified to be 84 and 55 respectively. Furthermore, the weighted gene co-expression network analysis (WGCNA) and the KEGG enrichment analysis result showed that folic acid supplementation affects inflammation and immune response-related pathways. The two groups have few pathways in common. One important lncRNA MSTRG.11108.1 and its target genes (ICAM1, CCL3, CCL4, etc.) were involved in immune-related pathways. Finally, through integrated analysis of lncRNAs with GWAS data and animal QTL database, we found that differential lncRNA and its target genes could be significantly enriched in SNPs and QTLs related to somatic cell count (SCC) and mastitis, such as MSTRG.11108.1 and its target gene ICAM1, CXCL3, GRO1.

CONCLUSIONS

For subclinical mastitic cows, folic acid supplementation can significantly affect the expression of immune-related pathway genes such as ICAM1 by regulating lncRNAs MSTRG.11108.1, thereby affecting related immune phenotypes. Our findings laid a ground foundation for theoretical and practical application for feeding folic acid supplementation in subclinical mastitic cows.

Guanylate-binding proteins signature predicts favorable prognosis, immune-hot microenvironment, and immunotherapy response in hepatocellular carcinoma

BACKGROUND

The role of guanylate-binding proteins (GBPs) in various cancers has been elucidated recently. However, our knowledge of the clinical relevance and biological characteristics of GBPs in hepatocellular carcinoma (HCC) remains limited.

METHODS

A total of 955 HCC patients were enrolled from five independent public HCC cohorts. The role of GBP molecules in HCC was preliminarily investigated, and a GBP family signature, termed GBPs-score, was constructed by principal component analysis to combine the GBP molecule values. We revealed the effects of GBP genes and GBPs-score in HCC via well-established bioinformatics methods and validated GBP1-5 experimentally in a tissue microarray (TMA) cohort.

RESULTS

GBPs molecules were closely associated with the prognosis of patients with HCC, and a high GBPs-score highly inferred a favorable survival outcome. We also revealed high GBPs-score was related to anti-tumor immunity, the immune-hot tumor microenvironment (TME), and immunotherapy response. Among the GBPs members, GBP1-5 rather than GBP6/7 may be dominant in these fields. The TMA analysis based on immunohistochemistry showed positive correlations between GBP1-5 and the immune-hot TME with abundant infiltration of CD8+ T cells in HCC.

CONCLUSIONS

Our integrative study revealed the genetic and immunologic characterizations of GBPs in HCC and highlighted their potential values as promising biomarkers for prognosis and immunotherapy.

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.

Multi-omics analysis of Siglec family genes in cutaneous melanoma

Background

Melanoma is widely recognized as the most aggressive and fatal type of skin cancer; however, effective prognostic markers are lacking. The sialic acid-binding immunoglobulin-type lectin (Siglec) gene family plays an important role in the development of tumors and immune escape, but its prognostic role in melanoma remains unknown.

Results

Siglec genes have a high mutation frequency, with up to 8% in SIGLEC7. High expression levels of Siglecs in tumor bulk suggests a better prognosis. Siglecs also show a high degree of synergistic expression. Immunohistochemistry was used to analyze the expression of SIGLEC9 in tumor tissue microarray. The expression of SIGLEC9 in tumor tissue without metastasis was higher than that in tumor tissue with metastasis. We used unsupervised clustering to create a high expression of Siglec (HES) cluster and a low expression of Siglec (LES) cluster. The HES cluster correlated with high overall survival and increased expression levels of Siglec genes. The HES cluster also showed significant immune cell infiltration and activation of immune signaling pathways. We used least absolute shrinkage and selection operator (LASSO) regression analysis to reduce the dimensionality of Siglec cluster-related genes and constructed a prognostic model composed of SRGN and GBP4, which can risk-stratify patients in both the training and test datasets.

Conclusion

We conducted a multi-omics analysis of the Siglec family genes in melanoma and found that Siglecs play an important role in the occurrence and development of melanoma. Typing constructed using Siglecs can show risk stratification and derived prognostic models can predict a patient's risk score. In summary, Siglec family genes are potential targets for melanoma treatment as well as prognostic markers that can direct individualized treatments and improve overall survival.

Increased Expression of the RBPMS Splice Variants Inhibits Cell Proliferation in Ovarian Cancer Cells

RNA-Binding Protein with Multiple Splicing (RBPMS) is a member of family proteins that bind to nascent RNA transcripts and regulate their splicing, localization, and stability. Evidence indicates that RBPMS controls the activity of transcription factors associated with cell growth and proliferation, including AP-1 and Smads. Three major RBPMS protein splice variants (RBPMSA, RBPMSB, and RBPMSC) have been described in the literature. We previously reported that reduced RBPMS levels decreased the sensitivity of ovarian cancer cells to cisplatin treatment. However, little is known about the biological role of the RBPMS splice variants in ovarian cancer cells. We performed RT-PCR and Western blots and observed that both RBPMSA and RBPMSC are reduced at the mRNA and protein levels in cisplatin resistant as compared with cisplatin sensitive ovarian cancer cells. The mRNA and protein levels of RBPMSB were not detectable in any of the ovarian cancer cells tested. To better understand the biological role of each RBPMSA and RBPMSC, we transfected these two splice variants in the A2780CP20 and OVCAR3CIS cisplatin resistant ovarian cancer cells and performed cell proliferation, cell migration, and invasion assays. Compared with control clones, a significant reduction in the number of colonies, colony size, cell migration, and invasion was observed with RBPMSA and RBPMSC overexpressed cells. Moreover, A2780CP20-RBPMSA and A2780CP20-RBPMSC clones showed reduced senescence-associated β-galactosidase (β-Gal)-levels when compared with control clones. A2780CP20-RBPMSA clones were more sensitive to cisplatin treatment as compared with A2780CP20-RBPMSC clones. The A2780CP20-RBPMSA and A2780CP20-RBPMSC clones subcutaneously injected into athymic nude mice formed smaller tumors as compared with A2780CP20-EV control group. Additionally, immunohistochemical analysis showed lower proliferation (Ki67) and angiogenesis (CD31) staining in tissue sections of A2780CP20-RBPMSA and A2780CP20-RBPMSC tumors compared with controls. RNAseq studies revealed many common RNA transcripts altered in A2780CP20-RBPMSA and A2780CP20-RBPMSC clones. Unique RNA transcripts deregulated by each RBPMS variant were also observed. Kaplan-Meier (KM) plotter database information identified clinically relevant RBPMSA and RBPMSC downstream effectors. These studies suggest that increased levels of RBPMSA and RBPMSC reduce cell proliferation in ovarian cancer cells. However, only RBPMSA expression levels were associated with the sensitivity of ovarian cancer cells to cisplatin treatment.

Identification of Melanoma Subsets Based on DNA Methylation Sites and Construction of a Prognosis Evaluation Model

Background

Melanoma is a lethal skin malignant tumor, and its formation or development is regulated by various genetic and epigenetic molecules. Although there are traditional methods provided for the doctors to evaluate the patients' prognosis or make the diagnosis, the novel method based on epigenetic markers is still needed to make the early diagnosis.

Results

We identified 256 melanoma-independent prognosis-related methylation sites (P < 0.0001) and divided patients into seven methylation subgroups. Methylation levels and survival time in the C2 subgroup were lower than that of other clusters (P < 0.05). We established the predicted model of prognosis risk for melanoma using the significantly changed methylation sites in C2. The model efficiently divided patients into high- and low-risk groups (area under the receiver operating characteristic curve, 0.833). Risk scores and patient survival time were negatively correlated (r_s = −0.325, P < 0.0001). Genes corresponding to the independent prognosis-associated methylation sites were enriched in cancer- and immunology-related pathways. We identified 35 hub genes. DOK2, GBP4, PSMB9, and NLRC5 were significantly changed according to methylation subgroups, survival, tumor stages, and T categories and were positively correlated, which was validated in the testing group (P < 0.05). The levels of DOK2, GBP4, PSMB9, and NLRC5 had an opposite trend to their methylation sites in patients with poor prognosis.

Conclusions

We identified seven DNA methylation subtypes and constructed a highly effective prognosis risk assessment model. The transcript levels of key genes corresponding to the independent prognosis-related methylation sites were significantly changed in patients according to prognosis and positively correlated with each other, indicating they may collaboratively promote melanoma formation. These findings further our understanding of the mechanism of melanoma and provide new targets for diagnosis and treatment.

The inflammatory signature in monocytes of Sjögren's syndrome and systemic lupus erythematosus, revealed by the integrated Reactome and drug target analysis

BACKGROUND

The innate immune regulation, especially by the type I IFN signature in the CD14+ monocytes, is known to be critical in the pathogenesis of autoimmune Sjögren's syndrome (SjS) and systemic lupus erythematosus (SLE).

OBJECTIVE

Since patients with one condition can be overlapped with another, this study is to identify shared differentially expressed genes (DEGs) in SjS and SLE compared to healthy controls (HCs) and refine transcriptomic profiles with the integrated Reactome and gene-drug network analysis for an anti-inflammation therapy.

METHODS

CD14+ monocytes were purified from whole blood of SjS and SLE patients (females, ages from 32 to 62) and subject to bulk RNA-sequencing, followed by data analyses for comparison with HC monocytes (females, ages 30 and 33). Functional categorizations, using Gene Ontology (GO) and the Reactome pathway analysis, were performed and DEGs associated with therapeutic drugs were identified from the Drug Repurposing Hub (DHUB) database.

RESULTS

The GO analysis revealed that DEGs in the inflammatory response and the cellular response to cytokine were highly enriched in both conditions. A propensity toward M1 macrophage differentiation appears to be prominent in SjS while the Response to Virus was significant in SLE monocytes. Through the Reactome pathway analysis, DEGs in the IFN signaling and the cytokine signaling in immune system were most significantly enriched in both. Upregulation of NGF-induced transcription activity in SjS and the complement cascade activity in SLE were also noted. Multiple anti-inflammatory drugs, such as prostaglandin-endoperoxide synthase and angiotensin-I-converting- enzyme were associated with the DEGs in these conditions.

CONCLUSIONS

Taken together, our analysis indicates distinct inflammatory transcriptomic profiles shared in SjS and SLE monocytes. Comprehensive characterizations of the data from these conditions will ultimately allow differential diagnosis of each condition and identification of therapeutic targets.

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.

Precise Chemodynamic Therapy of Cancer by Trifunctional Bacterium-Based Nanozymes

Chemodynamic therapy (CDT) destroys cancer cells by converting H₂O₂ or O₂ into reactive oxygen species (ROS), but its therapeutic efficacy is restricted by the antioxidant capacity of tumor. Previous solutions focused on strengthening the nanodrugs with the ability to increase ROS production or weaken the antioxidant capacity of cancer cells. Conversely, we here develop a mild nanodrug with negligible side effects. Specifically, the Au@Pt nanozyme decorated on a bacterial surface (Bac-Au@Pt) is reported to achieve precise CDT. Due to the tumor targeting ability of bacteria and catalytic property of Au@Pt nanozyme under acidic conditions, this nanosystem can release ROS to tumor cells effectively. In addition, the interferon gamma released by T cells specifically decreases the intracellular reductants in tumor cells, while having no obvious effect on normal cells. Therefore, a low dose of Bac-Au@Pt achieves a satisfactory therapeutic efficacy to tumor cells and is nontoxic to normal cells even at their acidic components. This nanosystem enables CDT and immunotherapy to mutually benefit and improve by each other, providing a promising strategy to achieve high anticancer efficacy even with a low dose usage.

Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification

Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those in melanoma patients. Therefore, the identification of more reliable prognosis biomarkers is urgently essential. Recent studies have shown that low immune cells infiltration is significantly associated with unfavorable clinical outcome in melanoma patients. Here we constructed a prognostic-related gene signature for melanoma risk stratification by quantifying the levels of several cancer hallmarks and identify the Wnt/β-catenin activation pathway as a primary risk factor for low tumor immunity. A series of bioinformatics and statistical methods were combined and applied to construct a Wnt-immune-related prognosis gene signature. With this gene signature, we computed risk scores for individual patients that can predict overall survival. To evaluate the robustness of the result, we validated the signature in multiple independent GEO datasets. Finally, an overall survival-related nomogram was established based on the gene signature and clinicopathological features. The Wnt-immune-related prognostic risk score could better predict overall survival compared with standard clinicopathological features. Our results provide a comprehensive map of the oncogene-immune-related gene signature that can serve as valuable biomarkers for better clinical decision making.

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.

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.

Identification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes

Background: Ovarian cancer is highly malignant and has a poor prognosis in the advanced stage. Studies have shown that infiltration of tumor microenvironment cells, immune cells and stromal cells has an important impact on the prognosis of cancers. However, the relationship between tumor microenvironment genes and the prognosis of ovarian cancer has not been studied.

Methods: Gene expression profiles and SNP data of ovarian cancer were downloaded from the TCGA database. Cluster analysis, WGCNA analysis and univariate survival analysis were used to identify immune microenvironment genes as prognostic signatures for predicting the survival of ovarian cancer patients. External data were used to evaluate the signature. Moreover, the top five significantly correlated genes were evaluated by immunohistochemical staining of ovarian cancer tissues.

Results: We systematically analyzed the relationship between ovarian cancer and immune metagenes. Immune metagenes expression were associated with prognosis. In total, we identified 10 genes related to both immunity and prognosis in ovarian cancer according to the expression of immune metagenes. These data reveal that high expression of ETV7 (OS, HR = 1.540, 95% CI 1.023–2.390, p = 0.041), GBP4 (OS, HR = 1.834, 95% CI 1.242–3.055, p = 0.004), CXCL9 (OS, HR = 1.613, 95% CI 1.080 –2.471, p = 0.021), CD3E (OS, HR = 1.590, 95% CI 1.049 –2.459, p = 0.031), and TAP1 (OS, HR = 1.766, 95% CI 1.163 –2.723, p = 0.009) are associated with better prognosis in patients with ovarian cancer.

Conclusion: Our study identified 10 immune microenvironment genes related to the prognosis of ovarian cancer. The list of tumor microenvironment-related genes provides new insights into the underlying biological mechanisms driving the tumorigenesis of ovarian cancer.

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.

Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.

Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).

Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.

Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

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.

Establishment and validation of a prognostic nomogram based on a novel five-DNA methylation signature for survival in endometrial cancer patients

Background

This study aimed to explore the prognostic role of DNA methylation pattern in endometrial cancer (EC) patients.

Methods

Differentially methylated genes (DMGs) of EC patients with distinct survival from The Cancer Genome Atlas (TCGA) database were analyzed to identify methylated genes as biomarkers for EC prognosis. The Least Absolute Shrinkage and Selection Operator (LASSO) analysis was used to construct a risk score model. A nomogram was built based on analysis combining the risk score model with clinicopathological signatures together, and then verified in the validation cohort and patients in our own center.

Results

In total, 157 DMGs were identified between different prognostic groups. Based on the LASSO analysis, five genes (GBP4, OR8K3, GABRA2, RIPPLY2, and TRBV5‐7) were screened for the establishment of risk score model. The model outperformed in prognostic accuracy at varying follow‐up times (AUC for 3 years: 0.824, 5 years: 0.926, and 7 years: 0.853). Multivariate analysis identified four independent risk factors including menopausal status (HR = 3.006, 95%CI: 1.062–8.511, p = 0.038), recurrence (HR = 2.116, 95%CI: 1.061–4.379, p = 0.046), lymph node metastasis (LNM, HR = 3.465, 95%CI: 1.225–9.807, p = 0.019), and five‐DNA methylation risk model (HR = 3.654, 95%CI: 1.458–9.161, p = 0.006) in training cohort. The performance of the nomogram was good in the training (AUC = 0.828), validation (AUC = 0.866) and the whole cohorts (AUC = 0.843). Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan–Meier survival curve also proved to be significantly different (p < 0.01). The subgroup analysis in different stratifications indicated that the accuracy was high in different subgroups for age, histological type, tumor grade, and clinical stage (all p < 0.01).

Conclusions

Briefly, our work established and verified a five‐DNA methylation risk model, and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of EC patients for clinicians.

Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

Skin cutaneous melanoma (SKCM) is an aggressive form of skin cancer that results in high mortality rate worldwide. It is vital to discover effective prognostic biomarkers and therapeutic targets for the treatment of melanoma. Long non-coding RNA (lncRNA) has been verified to play an essential role in the regulation of gene expression in diseases and tumors. Therefore, it is significant to explore the function of lncRNAs in the development and progression of SKCM. In this paper, a set of differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were first screened out using 471 cutaneous melanoma samples and 813 normal skin samples. Gene Ontology and KEGG pathway enrichment analysis were performed to obtain the significant function annotations and pathways of DEmRNAs. We also ran survival analysis on both DElncRNAs and DEmRNAs to identify prognostic-related lncRNAs and mRNAs. Next, a set of hub genes derived from protein-protein interaction (PPI) network analysis and lncRNA target genes screened from starbase-ENCORI database were integrated to construct a lncRNA-mRNA regulatory module, which includes 6 lncRNAs 4 target mRNAs. We further checked the capacity of these lncRNA and mRNA in the diagnosis of melanoma, and found that single lncRNA can effectively distinguish tumor and normal tissue. Moreover, we ran CMap analysis to select a list of small molecule drugs for SKCM, such as EGFR inhibitor AG-490, growth factor receptor inhibitor GW-441756 and apoptosis stimulant betulinic-acid, which have shown therapeutic effect in the treatment of melanoma.

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.

M1 Polarization Markers Are Upregulated in Basal-Like Breast Cancer Molecular Subtype and Associated With Favorable Patient Outcome

Background

Breast cancer heterogeneity is an essential element that plays a role in the therapy response variability and the patient's outcome. This highlights the need for more precise subtyping methods that focus not only on tumor cells but also investigate the profile of stromal cells as well as immune cells.

Objectives

To mine publicly available transcriptomic breast cancer datasets and reanalyze their transcriptomic profiling using unsupervised clustering in order to identify novel subsets in molecular subtypes of breast cancer, then explore the stromal and immune cells profile in each subset using bioinformatics and systems immunology approaches.

Materials and Methods

Transcriptomic data from 1,084 breast cancer patients obtained from The Cancer Genome Atlas (TCGA) database were extracted and subjected to unsupervised clustering using a recently described, multi-step algorithm called Iterative Clustering and Guide-gene Selection (ICGS). For each cluster, the stromal and immune profile was investigated using ESTIMATE and CIBERSORT analytical tool. Clinical outcomes and differentially expressed genes of the characterized clusters were identified and validated in silico and in vitro in a cohort of 80 breast cancer samples by immunohistochemistry.

Results

Seven unique sub-clusters showed distinct molecular and clinical profiles between the well-known breast cancer subtypes. Those unsupervised clusters identified more homogenous subgroups in each of the classical subtypes with a different prognostic profile. Immune profiling of the identified clusters showed that while the classically activated macrophages (M1) are correlated with the more aggressive basal-like breast cancer subtype, the alternatively activated macrophages (M2) showed a higher level of infiltration in luminal A and luminal B subtypes. Indeed, patients with higher levels of M1 expression showed less advanced disease and better patient outcomes presented as prolonged overall survival. Moreover, the M1 high basal-like breast cancer group showed a higher expression of interferon-gamma induced chemokines and guanylate-binding proteins (GBPs) involved in immunity against microbes.

Conclusion

Adding immune profiling using transcriptomic data can add precision for diagnosis and prognosis and can cluster patients according to the available modalities of therapy in a more personalized approach.

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.

Immune Microenvironment Related Competitive Endogenous RNA Network as Powerful Predictors for Melanoma Prognosis Based on WGCNA Analysis

Cutaneous melanoma is the most life-threatening skin malignant tumor due to its increasing metastasis and mortality rate. The abnormal competitive endogenous RNA network promotes the development of tumors and becomes biomarkers for the prognosis of various tumors. At the same time, the tumor immune microenvironment (TIME) is of great significance for tumor outcome and prognosis. From the perspective of TIME and ceRNA network, this study aims to explain the prognostic factors of cutaneous melanoma systematically and find novel and powerful biomarkers for target therapies. We obtained the transcriptome data of cutaneous melanoma from The Cancer Genome Atlas (TCGA) database, 3 survival-related mRNAs co-expression modules and 2 survival-related lncRNAs co-expression modules were identified through weighted gene co-expression network analysis (WCGNA), and 144 prognostic miRNAs were screened out by univariate Cox proportional hazard regression. Cox regression model and Kaplan-Meier survival analysis were employed to identify 4 hub prognostic mRNAs, and the prognostic ceRNA network consisting of 7 lncRNAs, 1 miRNA and 4 mRNAs was established. After analyzing the composition and proportion of total immune cells in cutaneous melanoma microenvironment through CIBERSORT algorithm, it is found through correlation analysis that lncRNA-TUG1 in the ceRNA network was closely related to the TIME. In this study, we first established cutaneous melanoma’s TIME-related ceRNA network by WGCNA. Cutaneous melanoma prognostic markers have been identified from multiple levels, which has important guiding significance for clinical diagnosis, treatment, and further scientific research on cutaneous melanoma.

Identification of CD38 as a potential biomarker in skin cutaneous melanoma using bioinformatics analysis

Skin cutaneous melanoma (SKCM) is the most aggressive type of skin cancer, with a high rate of metastasis and mortality; however, identification of biomarkers for the treatment of SKCM is required. Cluster of differentiation (CD)38 has emerged as an effective target for therapeutic drugs in several types of cancer, such as chronic lymphocytic leukemia and multiple myeloma. In the present study, to determine the contribution of CD38 to the diagnosis of SKCM, Gene Expression Profiling Interactive Analysis 2 and University of Alabama Cancer Database online tools were used to analyze The Cancer Genome Atlas-SKCM dataset. Moreover, Search Tool for the Retrieval of Interacting Genes/Proteins and GeneMANIA databases were used to determine protein-protein interaction networks and potential functions. To the best of our knowledge, the results of the present study indicated for the first time that high expression levels of CD38 were a favorable diagnostic factor for SKCM. Moreover, a correlation between CD38 expression levels and the survival probability of patients with SKCM was identified. Integrative analysis predicted that nine genes were correlated with CD38 in SKCM, and the similarity of these genes in SKCM expression and a survival heatmap was verified. Gene ontology enrichment analysis using the Metascape tool revealed that CD38 and its correlated genes were significantly enriched in lymphocyte activation and T cell differentiation regulation. Collectively, the bioinformatics analysis revealed that CD38 might serve as a potential diagnostic predictor for SKCM.

NGS Evaluation of Colorectal Cancer Reveals Interferon Gamma Dependent Expression of Immune Checkpoint Genes and Identification of Novel IFNγ Induced Genes

To evaluate the expression of immune checkpoint genes, their concordance with expression of IFNγ, and to identify potential novel ICP related genes (ICPRG) in colorectal cancer (CRC), the biological connectivity of six well documented ("classical") ICPs (CTLA4, PD1, PDL1, Tim3, IDO1, and LAG3) with IFNγ and its co-expressed genes was examined by NGS in 79 CRC/healthy colon tissue pairs. Identification of novel IFNγ- induced molecules with potential ICP activity was also sought. In our study, the six classical ICPs were statistically upregulated and correlated with IFNγ, CD8A, CD8B, CD4, and 180 additional immunologically related genes in IFNγ positive (FPKM > 1) tumors. By ICP co-expression analysis, we also identified three IFNγ-induced genes [(IFNγ-inducible lysosomal thiol reductase (IFI30), guanylate binding protein1 (GBP1), and guanylate binding protein 4 (GBP4)] as potential novel ICPRGs. These three genes were upregulated in tumor compared to normal tissues in IFNγ positive tumors, co-expressed with CD8A and had relatively high abundance (average FPKM = 362, 51, and 25, respectively), compared to the abundance of the 5 well-defined ICPs (Tim3, LAG3, PDL1, CTLA4, PD1; average FPKM = 10, 9, 6, 6, and 2, respectively), although IDO1 is expressed at comparably high levels (FPKM = 39). We extended our evaluation by querying the TCGA database which revealed the commonality of IFNγ dependent expression of the three potential ICPRGs in 638 CRCs, 103 skin cutaneous melanomas (SKCM), 1105 breast cancers (BC), 184 esophageal cancers (ESC), 416 stomach cancers (STC), and 501 lung squamous carcinomas (LUSC). In terms of prognosis, based on Pathology Atlas data, correlation of GBP1 and GBP4, but not IFI30, with 5-year survival rate was favorable in CRC, BC, SKCM, and STC. Thus, further studies defining the role of IFI30, GBP1, and GBP4 in CRC are warranted.

The FOXC2 Transcription Factor Promotes Melanoma Outgrowth and Regulates Expression of Genes Associated With Drug Resistance and Interferon Responsiveness

BACKGROUND/AIM

The FOXC2 transcription factor promotes the progression of several cancer types, but has not been investigated in the context of melanoma cells. To study FOXC2's influence on melanoma progression, we generated a FOXC2-deficient murine melanoma cell line and evaluated The Cancer Genome Atlas (TCGA) patient datasets.

MATERIALS AND METHODS

We compared tumor growth kinetics and RNA-seq/qRT-PCR gene expression profiles from wild-type versus FOXC2-deficient murine melanomas. We also performed Kaplan-Meier survival analysis of TCGA data to assess the influence of FOXC2 gene expression on melanoma patients' response to chemotherapy and immunotherapy.

RESULTS

FOXC2 promotes melanoma progression and regulates the expression of genes associated with multiple oncogenic pathways, including the oxidative stress response, xenobiotic metabolism, and interferon responsiveness. FOXC2 expression in melanoma correlates negatively with patient response to chemotherapy and immunotherapy.

CONCLUSION

FOXC2 drives a tumor-promoting gene expression program in melanoma and is a prognostic indicator of patient response to multiple cancer therapies.

Guanylate-Binding Protein 1: An Emerging Target in Inflammation and Cancer

Guanylate-binding protein 1 (GBP1) is a large GTPase of the dynamin superfamily involved in the regulation of membrane, cytoskeleton, and cell cycle progression dynamics. In many cell types, such as endothelial cells and monocytes, GBP1 expression is strongly provoked by interferon γ (IFNγ) and acts to restrain cellular proliferation in inflammatory contexts. In immunity, GBP1 activity is crucial for the maturation of autophagosomes infected by intracellular pathogens and the cellular response to pathogen-associated molecular patterns. In chronic inflammation, GBP1 activity inhibits endothelial cell proliferation even as it protects from IFNγ-induced apoptosis. A similar inhibition of proliferation has also been found in some tumor models, such as colorectal or prostate carcinoma mouse models. However, this activity appears to be context-dependent, as in other cancers, such as oral squamous cell carcinoma and ovarian cancer, GBP1 activity appears to anchor a complex, taxane chemotherapy resistance profile where its expression levels correlate with worsened prognosis in patients. This discrepancy in GBP1 function may be resolved by GBP1's involvement in the induction of a cellular senescence phenotype, wherein anti-proliferative signals coincide with potent resistance to apoptosis and set the stage for dysregulated proliferative mechanisms present in growing cancers to hijack GBP1 as a pro- chemotherapy treatment resistance (TXR) and pro-survival factor even in the face of continued cytotoxic treatment. While the structure of GBP1 has been extensively characterized, its roles in inflammation, TXR, senescence, and other biological functions remain under-investigated, although initial findings suggest that GBP1 is a compelling target for therapeutic intervention in a variety of conditions ranging from chronic inflammatory disorders to cancer.

Protective role of mouse mast cell tryptase Mcpt6 in melanoma

Tryptase-positive mast cells populate melanomas, but it is not known whether tryptase impacts on melanoma progression. Here we addressed this and show that melanoma growth is significantly higher in tryptase-deficient (Mcpt6^-/- ) versus wild-type mice. Histochemical analysis showed that mast cells were frequent in the tumor stroma of both wild-type and Mcpt6^-/- mice, and also revealed their presence within the tumor parenchyma. Confocal microscopy analysis revealed that tryptase was taken up by the tumor cells. Further, tryptase-positive granules were released from mast cells and were widely distributed within the tumor tissue, suggesting that tryptase could impact on the tumor microenvironment. Indeed, gene expression analysis showed that the absence of Mcpt6 caused decreased expression of numerous genes, including Cxcl9, Tgtp2, and Gbp10, while the expression of 5p-miR3098 was enhanced. The levels of CXCL9 were lower in serum from Mcpt6^-/- versus wild-type mice. In further support of a functional impact of tryptase on melanoma, recombinant tryptase (Mcpt6) was taken up by cultured melanoma cells and caused reduced proliferation. Altogether, our results indicate a protective role of mast cell tryptase in melanoma growth.

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.

Furin-mediated protein processing in infectious diseases and cancer

Proteolytic cleavage regulates numerous processes in health and disease. One key player is the ubiquitously expressed serine protease furin, which cleaves a plethora of proteins at polybasic recognition motifs. Mammalian substrates of furin include cytokines, hormones, growth factors and receptors. Thus, it is not surprising that aberrant furin activity is associated with a variety of disorders including cancer. Furthermore, the enzymatic activity of furin is exploited by numerous viral and bacterial pathogens, thereby enhancing their virulence and spread. In this review, we describe the physiological and pathophysiological substrates of furin and discuss how dysregulation of a simple proteolytic cleavage event may promote infectious diseases and cancer. One major focus is the role of furin in viral glycoprotein maturation and pathogenicity. We also outline cellular mechanisms regulating the expression and activation of furin and summarise current approaches that target this protease for therapeutic intervention.

A four-DNA methylation biomarker is a superior predictor of survival of patients with cutaneous melanoma

Cutaneous melanoma (CM) is a life-threatening form of skin cancer. Prognostic biomarkers can reliably stratify patients at initial melanoma diagnosis according to risk, and may inform clinical decisions. Here, we performed a retrospective, cohort-based study analyzing genome-wide DNA methylation of 461 patients with CM from the TCGA database. Cox regression analyses were conducted to establish a four-DNA methylation signature that was significantly associated with the overall survival (OS) of patients with CM, and that was validated in an independent cohort. Corresponding Kaplan-Meier analysis displayed a distinct separation in OS. The ROC analysis confirmed that the predictive signature performed well. Notably, this signature exhibited much higher predictive accuracy in comparison with known biomarkers. This signature was significantly correlated with immune checkpoint blockade (ICB) immunotherapy-related signatures, and may have potential as a guide for measures of responsiveness to ICB immunotherapy.