Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Unveiling Atherosclerotic Plaque Heterogeneity and SPP1+/VCAN+ Macrophage Subtype Prognostic Significance Through Integrative Single-Cell and Bulk-Seq Analysis

Background

Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.

Methods

This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.

Results

Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1+/VCAN+ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.

Conclusion

Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability.

Identification of crucial genes through WGCNA in the progression of gastric cancer

Background: To explore the hub gene closely related to the progression of gastric cancer (GC), so as to provide a theoretical basis for revealing the therapeutic mechanism of GC. Methods: The gene expression profile and clinical data of GSE15459 in Gene Expression Omnibus (GEO) database were downloaded. The weighted gene co-expression network analysis (WGCNA) was used to screen the key modules related to GC progression. Survival analysis was used to assess the influence of hub genes on patients' outcomes. CIBERSORT analysis was used to predict the tissue infiltrating immune cells in patients. Immunohistochemical staining was conducted to further verify the expression of hub genes. Results: Through WGCNA, a total of 26 co-expression modules were constructed, in which salmon module and royalblue module had strong correlation with GC progression. The results of enrichment analysis showed that genes in the two modules were mainly involved in toll-like receptor signaling pathway, cholesterol metabolism and neuroactive ligand-receptor interaction. Six hub genes (C1QA, C1QB, C1QC, FCER1G, FPR3 and TYROBP) related to GC progression were screened. Survival analysis showed overall survival in the high expression group was significantly lower than that in the low expression group. CIBERSORT analysis revealed that immune characteristics difference between patients in early stage and advanced stage. Immunohistochemical results confirmed that C1QB, FCER1G, FPR3 and TYROBP were significantly associated with disease progression in GC. Conclusion: Our study identified that C1QB, FCER1G, FPR3 and TYROBP played important roles in the progression of GC, and their specific mechanisms are worth further study.

Comparative gene regulatory networks modulating APOE expression in microglia and astrocytes

Background

Single-cell technologies have unveiled various transcriptional states in different brain cell types. Transcription factors (TFs) regulate the expression of related gene sets, thereby controlling these diverse expression states. Apolipoprotein E ( APOE ), a pivotal risk-modifying gene in Alzheimer's disease (AD), is expressed in specific glial transcriptional states associated with AD. However, it is still unknown whether the upstream regulatory programs that modulate its expression are shared across brain cell types or specific to microglia and astrocytes.

Methods

We used pySCENIC to construct state-specific gene regulatory networks (GRNs) for resting and activated cell states within microglia and astrocytes based on single-nucleus RNA sequencing data from AD patients' cortices from the Knight ADRC-DIAN cohort. We then identified replicating TF using data from the ROSMAP cohort. We identified sets of genes co-regulated with APOE by clustering the GRN target genes and identifying genes differentially expressed after the virtual knockout of TFs regulating APOE . We performed enrichment analyses on these gene sets and evaluated their overlap with genes found in AD GWAS loci.

Results

We identified an average of 96 replicating regulators for each microglial and astrocyte cell state. Our analysis identified the CEBP, JUN, FOS, and FOXO TF families as key regulators of microglial APOE expression. The steroid/thyroid hormone receptor families, including the THR TF family, consistently regulated APOE across astrocyte states, while CEBP and JUN TF families were also involved in resting astrocytes. AD GWAS-associated genes ( PGRN , FCGR3A , CTSH , ABCA1 , MARCKS , CTSB , SQSTM1 , TSC22D4 , FCER1G , and HLA genes) are co-regulated with APOE. We also uncovered that APOE-regulating TFs were linked to circadian rhythm ( BHLHE40 , DBP , XBP1 , CREM , SREBF1 , FOXO3 , and NR2F1 ).

Conclusions

Our findings reveal a novel perspective on the transcriptional regulation of APOE in the human brain. We found a comprehensive and cell-type-specific regulatory landscape for APOE , revealing distinct and shared regulatory mechanisms across microglia and astrocytes, underscoring the complexity of APOE regulation. APOE -co-regulated genes might also affect AD risk. Furthermore, our study uncovers a potential link between circadian rhythm disruption and APOE regulation, shedding new light on the pathogenesis of AD.

A novel CD8+ T cell-related gene signature as a prognostic biomarker in hepatocellular carcinoma

CD8+ T cells have great roles in tumor suppression and elimination of various tumors including hepatocellular carcinoma (HCC). Nonetheless, potential prognostic roles of CD8+ T cell-related genes (CD8Gs) in HCC remains unknown. In our study, 416 CD8Gs were identified in HCC, which were enriched in inflammatory and immune signaling pathways. Using The Cancer Genome Atlas dataset, a 5-CD8Gs risk model (KLRB1, FYN, IL2RG, FCER1G, and DGKZ) was constructed, which was verified in International Cancer Genome Consortium and gene expression omnibus datasets. Furthermore, we found that overall survival was independently correlated with the CD8Gs signature, and it was associated with immune- and cancer-related signaling pathways and immune cells infiltration. Finally, drug sensitivity data indicated that 10 chemotherapeutic drugs held promise as therapeutics for HCC patients with high-risk. In conclusion, multi-databases analysis showed that 5-CD8Gs and their signature could be an indicator to predict candidate drugs for HCC therapy.

Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology

COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.

Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys

T cells are important in the pathogenesis of acute kidney injury (AKI), and TCR+CD4-CD8- (double negative-DN) are T cells that have regulatory properties. However, there is limited information on DN T cells compared to traditional CD4+ and CD8+ cells. To elucidate the molecular signature and spatial dynamics of DN T cells during AKI, we performed single-cell RNA sequencing (scRNA-seq) on sorted murine DN, CD4+, and CD8+ cells combined with spatial transcriptomic profiling of normal and post AKI mouse kidneys. scRNA-seq revealed distinct transcriptional profiles for DN, CD4+, and CD8+ T cells of mouse kidneys with enrichment of Kcnq5, Klrb1c, Fcer1g, and Klre1 expression in DN T cells compared to CD4+ and CD8+ T cells in normal kidney tissue. We validated the expression of these four genes in mouse kidney DN, CD4+ and CD8+ T cells using RT-PCR and Kcnq5, Klrb1, and Fcer1g genes with the NIH human kidney precision medicine project (KPMP). Spatial transcriptomics in normal and ischemic mouse kidney tissue showed a localized cluster of T cells in the outer medulla expressing DN T cell genes including Fcer1g. These results provide a template for future studies in DN T as well as CD4+ and CD8+ cells in normal and diseased kidneys.

The Transcription Factor Twist1 Has a Significant Role in Mycosis Fungoides (MF) Cell Biology: An RNA Sequencing Study of 40 MF Cases

The purpose of this RNA sequencing study was to investigate the biological mechanism underlying how the transcription factors (TFs) Twist1 and Zeb1 influence the prognosis of mycosis fungoides (MF). We used laser-captured microdissection to dissect malignant T-cells obtained from 40 skin biopsies from 40 MF patients with stage I-IV disease. Immunohistochemistry (IHC) was used to determinate the protein expression levels of Twist1 and Zeb1. Based on RNA sequencing, principal component analysis (PCA), differential expression (DE) analysis, ingenuity pathway analysis (IPA), and hub gene analysis were performed between the high and low Twist1 IHC expression cases. The DNA from 28 samples was used to analyze the TWIST1 promoter methylation level. In the PCA, Twist1 IHC expression seemed to classify cases into different groups. The DE analysis yielded 321 significant genes. In the IPA, 228 significant upstream regulators and 177 significant master regulators/causal networks were identified. In the hub gene analysis, 28 hub genes were found. The methylation level of TWIST1 promoter regions did not correlate with Twist1 protein expression. Zeb1 protein expression did not show any major correlation with global RNA expression in the PCA. Many of the observed genes and pathways associated with high Twist1 expression are known to be involved in immunoregulation, lymphocyte differentiation, and aggressive tumor biology. In conclusion, Twist1 might be an important regulator in the disease progression of MF.

The Genetic and Immunologic Landscape Underlying the Risk of Malignant Progression in Laryngeal Dysplasia

(1) Background: The development of laryngeal cancer is a multistep process involving structural alterations of the epithelial mucosa, from dysplasia (LDy) to invasive carcinoma. In this study, we define new biomarkers, prognostic for malignant transformation, in patients affected by LDy. (2) Methods: We used targeted next-generation sequencing and immunohistochemical analysis to define the mutational and immunological landscape of 15 laryngeal dysplasia progressing to invasive cancer (progressing dysplasia), as well as 31 cases of laryngeal dysplasia that did not progress to carcinoma (non-progressing dysplasia). Two pathologists independently analyzed the presence of tumor-infiltrating lymphocytes in LDy pre-embedded paraffin-fixed specimens. The RNA-based next-generation sequencing panel OIRRA was used to evaluate the expression of 395 genes related to immune system activation. (3) Results: High TILs are significantly correlated with a higher risk of malignant transformation. The non-brisk pattern was significantly associated with an 86% reduced risk of malignant progression (OR = 0.16, 95% CI: 0.03-0.5, p = 0.008). TILs showed a highly positive correlation with CCR6, CD83, HLA-DPB1, MX1 and SNAI1, and they were inversely correlated with CD48, CIITA, CXCR4, FCER1G, IL1B, LST1 and TLR8. (4) Conclusions: TILs have a great potential to identify high-risk progression dysplasia and thus to define surveillance protocols and prevention programs.

Identification and Analysis of Crucial Genes in H. pylori-Associated Gastric Cancer Using an Integrated Bioinformatics Approach

Background

The relationship between H. pylori infection and gastric cancer (GC) has been widely studied, and H. pylori is considered as the main factor. Utilizing bioinformatics analysis, this study examined gene signatures related to progressing H. pylori-associated GC.

Materials and Methods

The dataset GSE13195 was chosen to search for abnormally expressed genes in H. pylori-associated GC and normal tissues. The TCGA-STAD database was chosen to verify the expression of key genes in GC and normal tissues.

Results

In GSE13195, a total of 332 differential expression genes (DEGs) were screened. The results of weighted gene co-expression network analysis showed that the light cyan, plum2, black, and magenta4 modules were associated with stages (T3, T2, and T4), while the orangered4, salmon2, pink, and navajowhite2 modules were correlated with lymph node metastasis (N3, N2, and N0). Based on the results of DEGs and hub genes, a total of 7 key genes (ADAM28, FCER1G, MRPL14, SOSTDC1, TYROBP, C1QC, and C3) were screened out. These gene mRNA levels were able to distinguish between normal and H. pylori-associated GC tissue using receiver operating characteristic curves. After transcriptional level verification and survival analysis, ADAM28 and C1QC were excluded. An immune infiltration study revealed that key genes were involved in regulating the infiltration levels of cells associated with innate immune response, antigen presentation process, humoral immune response, or Tcell-mediated immune response. In addition, drugs targeting FCER1G and TYROBP have been approved and are under investigation.

Conclusion

Our study identified five key genes involved in H. pylori-associated GC tumorigenesis. Patients with higher levels of C3 expression had a poorer prognosis than those with lower levels. In addition, these key genes may serve as biomarkers and therapeutic targets for H. pylori-associated GC diagnosis, targeted therapy, and immunotherapy in the future.

Fc Fragment of IgE Receptor Ig (FCER1G) acts as a key gene involved in cancer immune infiltration and tumour microenvironment

Although recent studies have revealed the relationship between Fc Fragment of IgE Receptor Ig (FCER1G) and human tumours, there is still a lack of a more comprehensive pan-cancer analysis of FCER1G as an immune-related gene. In this study, we investigated the expression pattern and prognostic value of FCER1G based on multiple databases. Subsequently, we further explored the role of FCER1G in tumour proliferation and metastasis, as well as its genomic alterations and DNA methylation levels, we next assessed the association between FCER1G and the immune infiltrating cells of the tumour microenvironment in different cancers and verified it by immunohistochemical staining. The correlation between FCER1G and immune checkpoint genes expression and its predictive power in the immune checkpoint blockade treatment cohorts were used to evaluate the importance of FCER1G in immunotherapy. Enrichment analysis of FCER1G-associated partners was also performed. In addition, we substantiated the expression of FCER1G in specific cell types of different tumours using single-cell RNA sequencing data from different databases. Our research results showed that FCER1G is up-regulated in most tumour. Positive associations were found between FCER1G expression and tumour prognosis, proliferation, and metastasis, we also found that FCER1G is closely related to the tumour microenvironment and tumour immunity. Moreover, FCER1G-associated partners were enriched in pathways associated with neutrophils activation. Finally, we confirmed that FCER1G was mainly expressed in monocyte/macrophages of the tumour microenvironment. In conclusion, our findings provided a comprehensive understanding of FCER1G in oncogenesis and tumour immunology among various tumours and demonstrated its potential value in prognosis prediction and tumour immunotherapy.

A Prognostic Nomogram for Hepatocellular Carcinoma Based on Wound Healing and Immune Checkpoint Genes

BACKGROUND AND AIMS

Wound healing and tumor progression share some common biological features; however, how variations in wound healing patterns affect hepatocellular carcinoma (HCC) prognosis remains unclear.

METHODS

We analyzed the wound healing patterns of 594 HCC samples from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and correlated them with immune infiltration and the expression levels of immune checkpoint genes. A risk score, which we named the "heal.immune" score, was established via stepwise Cox estimation. We constructed a nomogram based on age, sex, TNM stage, and heal.immune score and explored its predictive value for HCC prognosis. Seventy-four clinical patients were enrolled in this study, and all were from Huashan Hospital of Fudan University between 2015 and 2017 to serve as an independent validation group.

RESULTS

We identified two distinct wound healing patterns in HCC. The biological processes of healing cluster 1 (C1) are related to metabolism, while those of healing cluster 2 (C2) are related to the inflammatory response and immune cell accumulation. A total of 565 wound healing-related genes (based on Gene Ontology) and 25 immune checkpoint genes were considered. By analyzing differentially expressed genes and implementing a stepwise Cox estimation analysis, six genes with p values less than 0.02 in a multivariate Cox estimation were chosen as the "heal.immune" gene set (FCER1G, PLAT, ITGA5, CCNB1, CD86 and CD40). The "heal.immune" gene set, as an OS risk factor, was further validated in Fudan cohort. We constructed a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in the TCGA cohort. The area under curve vales of the receiver characteristic operator curves were 0.82, 0.76 and 0.73 in the training group and 0.84, 0.76 and 0.72 in the test group.

CONCLUSIONS

We established a prognostic nomogram based on the heal.immune gene signature, which includes six wound healing- and immunity-related genes. This nomogram accurately predicts the OS of HCC patients.

Antifungal immunity mediated by C-type lectin receptors may be a novel target in immunotherapy for urothelial bladder cancer

Immunotherapies, such as immune-checkpoint blockade and adoptive T-cell therapy, offer novel treatment options with good efficacy for patients with urothelial bladder cancer. However, heterogeneity and therapeutic resistance have limited the use of immunotherapy. Further research into immune-regulatory mechanisms in bladder cancer is urgently required. Emerging evidence demonstrates that the commensal microbiota and its interactions with host immunity play pivotal roles in a variety of physiological and pathological processes, including in cancer. The gut microbiota has been identified as a potentially effective target of treatment that can be synergized with immunotherapy. The urothelial tract is also a key site for multiple microbes, although the immune-regulatory role of the urinary microbiome in the process of carcinogenesis of bladder cancer remains to be elucidated. We performed a comprehensive analysis of the expression and biological functions of C-type lectin receptors (CLRs), which have been recognized as innate pathogen-associated receptors for fungal microbiota, in bladder cancer. In line with previous research on fungal colonization of the urothelial tract, we found that CLRs, including Dectin-1, Dectin-2, Dectin-3, and macrophage-inducible Ca²⁺-dependent lectin receptor (Mincle), had a significant association with immune infiltration in bladder cancer. Multiple innate and adaptive pathways are positively correlated with the upregulation of CLRs. In addition, we found a significant correlation between the expression of CLRs and a range of immune-checkpoint proteins in bladder cancer. Based on previous studies and our findings, we hypothesize that the urinary mycobiome plays a key role in the pathogenesis of bladder cancer and call for more research on CLR-mediated anti-fungal immunity against bladder cancer as a novel target for immunotherapy in urothelial bladder cancer.

FCER1G Gene Hypomethylation in Patients with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease that, when improperly treated, leads to disability in patients. Various factors that may cause the development and activity of RA are being considered. Epigenetic factors are also receiving increasing attention. In our study, we analyzed the association between FCER1G gene methylation and RA activity. We conducted our study in 50 RA patients and 24 controls. The patients were divided into two groups in terms of high disease activity and remission. Quantitative real-time methylation-specific PCR was used to analyze the methylation status of the investigated genes. We observed that RA patients have lower levels of methylation of the FCER1G gene compared to controls, but we did not find any difference in the methylation status of this gene between patients with high disease activity and remission. The results of this study suggest that FCER1G gene methylation may be a new potential epigenetic marker of RA that is independent of disease activity.

Identification of FCER1G related to Activated Memory CD4+ T Cells Infiltration by Gene Co-expression Network and Construction of a Risk Prediction Module in Diffuse Large B-Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4⁺ T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4⁺ T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4⁺ T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4⁺ T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL.

Systems medicine dissection of chr1q-amp reveals a novel PBX1-FOXM1 axis for targeted therapy in multiple myeloma

Understanding the biological and clinical impact of copy number aberrations (CNAs) on the development of precision therapies in cancer remains an unmet challenge. Genetic amplification of chromosome 1q (chr1q-amp) is a major CNA conferring an adverse prognosis in several types of cancer, including in the blood cancer multiple myeloma (MM). Although several genes across chromosome 1 (chr1q) portend high-risk MM disease, the underpinning molecular etiology remains elusive. Here, with reference to the 3-dimensional (3D) chromatin structure, we integrate multi-omics data sets from patients with MM with genetic variables to obtain an associated clinical risk map across chr1q and to identify 103 adverse prognosis genes in chr1q-amp MM. Prominent among these genes, the transcription factor PBX1 is ectopically expressed by genetic amplification and epigenetic activation of its own preserved 3D regulatory domain. By binding to reprogrammed superenhancers, PBX1 directly regulates critical oncogenic pathways and a FOXM1-dependent transcriptional program. Together, PBX1 and FOXM1 activate a proliferative gene signature that predicts adverse prognosis across multiple types of cancer. Notably, pharmacological disruption of the PBX1-FOXM1 axis with existing agents (thiostrepton) and a novel PBX1 small molecule inhibitor (T417) is selectively toxic against chr1q-amp myeloma and solid tumor cells. Overall, our systems medicine approach successfully identifies CNA-driven oncogenic circuitries, links them to clinical phenotypes, and proposes novel CNA-targeted therapy strategies in MM and other types of cancer.

FCER1G positively relates to macrophage infiltration in clear cell renal cell carcinoma and contributes to unfavorable prognosis by regulating tumor immunity

Background

Tumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.

Methods

TCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance.

Results

In this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers: STAC3, LGALS9, TREM2, FCER1G, and PILRA. Among them, FCER1G was abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels of FCER1G are related to T cell suppression. Moreover, combination of FCER1G and macrophage biomarker CD68 can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.

Conclusions

High expression of FCER1G in ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09251-7.

Obesity-Specific Association of Statin Use and Reduced Risk of Recurrence of Early Stage NSCLC

Introduction

Statins, used for their lipid-lowering activity, have anti-inflammatory and anticancer properties as well. We evaluated this potential benefit of statin use in patients with NSCLC.

Methods

All 613 patients with pathologic stage 1 or 2 NSCLC who had lobectomy without neoadjuvant therapy at our institution during 2008 to 2015 were included. Association between presurgery statin use and overall survival and recurrence-free survival (RFS) was analyzed using Cox proportional hazards regression. Association of statin use with tumor transcriptome was evaluated in another 350 lung cancer cases.

Results

Univariable analyses did not reveal a statistically significant association of statin use with either overall survival or RFS, with hazard ratio equals to 1.19 and 0.70 (Wald p = 0.28 and 0.09), respectively. In subgroup analyses, significantly improved RFS was found in statin users, but only in overweight/obese patients (body mass index [BMI] > 25; n = 422), with univariable and multivariable hazard ratio of 0.49 and 0.46 (p = 0.005 and 0.002), respectively, but not in patients with BMI less than or equal to 25 (n = 191; univariable p = 0.21). Transcriptomes of tumor statin users had high expression of tumoricidal genes such as granzyme A and interferon-γ compared with those of nonusers among high- but not low-BMI patients with lung cancer.

Conclusions

Our study suggests that statins may improve the outcome of early stage NSCLC but only in overweight or obese patients. This benefit may stem from a favorable reprogramming of the antitumor immune response that statins perpetrate specifically in the obese.

A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

BACKGROUND

Gastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.

METHODS

Weighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.

RESULTS

WGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.

CONCLUSIONS

Our results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

Development and validation of nomogram with tumor microenvironment-related genes and clinical factors for predicting overall survival of endometrial cancer

Background: Tumor microenvironment (TME) has attracted lots of attention with its important role in the tumor development. This study aimed to explore TME- related genes of prognostic value in patients with endometrial cancer (EC) and establish a prediction model for EC.

Methods: The RNA-Seq data and clinicopathological characteristics of 521 subjects were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified based on the immune and stromal scores, which were calculated by the ESTIMATE algorithm. Hub genes were initially screened using the Cytoscape and further selected through Cox regression. Gene correlation analysis was performed in TIMER database. A nomogram was constructed integrating prognosis-related hub genes and clinical factors and validated in the validation group. Risk stratification was performed based on the nomogram.

Results: Three TME-related hub genes (CCR5, FCER1G, and ITGB2) were found with significant prognostic value for EC patients. The expression of CCR5, FCER1G, and ITGB2 were significantly correlated with various immune cells infiltration. Based on the Cox regression, a nomogram was constructed by integrating five predictors (stage, grade, immune score, expression of FCER1G, and ITGB2), with a C-index of 0.765. Discrimination of the model was confirmed in the validation group (C-index: 0.716). The calibration curves for the 3- and 5- year survival indicated good calibration. Patients in high- and low- risk groups presented significantly different survival outcomes (P<0.001) in both discovery and validation group.

Conclusion: TME-related hub genes of prognostic value identified in our study may provide references for the mechanisms underlying EC development and the immunotherapy for EC. The prediction model may help assess the prognosis of EC patients.

MMRFBiolinks: an R-package for integrating and analyzing MMRF-CoMMpass data

In order to understand the mechanisms underlying the onset and the drug responses in multiple myeloma (MM), the second most frequent hematological cancer, the use of appropriate bioinformatic tools for integrative analysis of publicly available genomic data is required. We present MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and from the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. The package provides several methods for integrative analysis (array-array intensity correlation, Kaplan-Meier survival analysis) and visualization (response to treatments plot) of MMRF data, for performing an easily comprehensible analysis workflow. MMRFBiolinks extends the TCGABiolinks package by providing 13 new functions to analyze MMRF-CoMMpass data: six dealing with MMRF-RG data and seven with NCI-GDC data. As validation of the tool, we present two cases studies for searching, downloading and analyzing MMRF data. The former presents a workflow for identifying genes involved in survival depending on treatment. The latter presents an analysis workflow for analyzing the Best Overall (BO) response through correlation plots between the BO Response with respect to treatments, time, duration of treatment and annotated variants, as well as through Kaplan-Meier survival curves. The case studies demonstrate how MMRFBiolinks is able of overcoming the limitations of the analysis tools available at NCI-GDC and MMRF-RG, facilitating and making more comprehensive the retrieval, downloading and analysis of MMRF data.

Prognostic and predictive value of FCER1G in glioma outcomes and response to immunotherapy

PURPOSE

Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma.

METHODS

The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups.

RESULTS

FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI  1.04-1.52), grade (HR = 2.75, 95 % CI 2.06-3.68), tumor recurrence (HR = 2.17, 95 % CI  1.81-2.62), IDH mutant (HR = 2.46, 95 % CI 1.97-3.01) and chemotherapeutic status (HR = 1.4, 95 % CI  1.20-1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients.

CONCLUSIONS

This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy.

PLK1 and NOTCH Positively Correlate in Melanoma and Their Combined Inhibition Results in Synergistic Modulations of Key Melanoma Pathways

Melanoma is one of the most serious forms of skin cancer, and its increasing incidence coupled with nonlasting therapeutic options for metastatic disease highlights the need for additional novel approaches for its management. In this study, we determined the potential interactions between polo-like kinase 1 (PLK1, a serine/threonine kinase involved in mitotic regulation) and NOTCH1 (a type I transmembrane protein deciding cell fate during development) in melanoma. Employing an in-house human melanoma tissue microarray (TMA) containing multiple cases of melanomas and benign nevi, coupled with high-throughput, multispectral quantitative fluorescence imaging analysis, we found a positive correlation between PLK1 and NOTCH1 in melanoma. Furthermore, The Cancer Genome Atlas database analysis of patients with melanoma showed an association of higher mRNA levels of PLK1 and NOTCH1 with poor overall, as well as disease-free, survival. Next, utilizing small-molecule inhibitors of PLK1 and NOTCH (BI 6727 and MK-0752, respectively), we found a synergistic antiproliferative response of combined treatment in multiple human melanoma cells. To determine the molecular targets of the overall and synergistic responses of combined PLK1 and NOTCH inhibition, we conducted RNA-sequencing analysis employing a unique regression model with interaction terms. We identified the modulations of several key genes relevant to melanoma progression/metastasis, including MAPK, PI3K, and RAS, as well as some new genes such as Apobec3G, BTK, and FCER1G, which have not been well studied in melanoma. In conclusion, our study demonstrated a synergistic antiproliferative response of concomitant targeting of PLK1 and NOTCH in melanoma, unraveling a potential novel therapeutic approach for detailed preclinical/clinical evaluation.

Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Glioma is the most common malignant tumor in the central nervous system. Evidence shows that clinical efficacy of immunotherapy is closely related to the tumor microenvironment. This study aims to establish a microenvironment-related genes (MRGs) model to predict the prognosis of patients with Grade II/III gliomas. Gene expression profile and clinical data of 459 patients with Grade II/III gliomas were extracted from The Cancer Genome Atlas. Then according to the immune/stromal scores generated by the ESTIMATE algorithm, the patients were scored one by one. Weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network to identify potential biomarkers for predicting the prognosis of patients. When adjusting clinical features including age, histology, grading, IDH status, we found that these features were independently associated with survival. The predicted value of the prognostic model was then verified in 440 samples in CGGA part B dataset and 182 samples in CGGA part C dataset by univariate and multivariate cox analysis. The clinical samples of 10 patients further confirmed our signature. Our findings suggested the eight-MRGs signature identified in this study are valuable prognostic predictors for patients with Grade II/III glioma.