Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

IGF2BP2 orchestrates global expression and alternative splicing profiles associated with glioblastoma development in U251 cells

Glioblastoma (GBM) is a highly invasive and malignant central nervous system tumor with a median survival duration of 15 months despite multimodal therapy. The insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) has been implicated in various cancers and is known to regulate RNA metabolism and alternative splicing (AS). However, its role in GBM remains unclear. Overexpression of IGF2BP2 led to significant alterations in gene expression, with 472 genes upregulated and 99 downregulated. Gene ontology (GO) analysis indicated enrichment in immune-related biological processes. Notably, IGF2BP2 was found to regulate AS events, with 1372 regulated AS genes (RASGs) and 2096 significantly distinct ASEs identified. Furthermore, IGF2BP2 selectively bound to 3' and 5' untranslated regions (UTRs) via GG[AU]C motifs, and IFIH1 was identified as a direct binding partner and upregulated gene upon IGF2BP2 overexpression. Functional enrichment analysis suggested that IGF2BP2 influences pathways related to RNA splicing and immune responses. Our findings demonstrate that IGF2BP2 plays a crucial role in GBM by modulating the transcriptome and AS events. The upregulation of immune-related genes and the regulation of AS by IGF2BP2 highlight its potential as a therapeutic target in GBM, particularly for immunotherapy. The study provides a foundation for further investigation into the molecular mechanisms of IGF2BP2 in GBM and its implications for cancer treatment.

DSN1 may predict poor prognosis of lower-grade glioma patients and be a potential target for immunotherapy

DSN1 has been previously found to be positively correlated with various cancers. However, the effect of DSN1 or its methylation on the prognosis, molecular characteristics, and immune cell infiltration of low-grade glioma (LGG) has not yet been studied. We obtained 1046 LGG samples from the The Cancer Genome Atlas, The Chinese Glioma Genome Atlas (CGGA) microarray, and CGGA RNA-Seq databases. Bioinformatic methods (gene set enrichment analysis (GSEA), chi-square test, multivariate), and laboratory validation were used to investigate DSN1 in LGG. The expression levels of DSN1 mRNA and protein in LGG were substantially higher than those in normal brain tissue, and their expression was negatively regulated by methylation. The survival time of patients with low expression of DSN1 and cg12601032 hypermethylation was considerably prolonged. DSN1 was a risk factor, and of good diagnostic and prognostic value for LGG. Importantly, the expression of DSN1 is related to many types of tumor-infiltrating immune cells and has a positive correlation with PDL1. DSN1 promoted the activation of multiple cancer-related pathways, such as the cell cycle. Additionally, knockdown of DSN1 substantially inhibited the proliferation and invasion of LGG cells. To the best of our knowledge, this study is the first comprehensive analysis of the mechanism of DSN1 leading to poor prognosis of LGG, which provides a new perspective for revealing the pathogenesis of LGG. DSN1 or its methylation has diagnostic value for the prognosis of glioma, and may become a new biological target of anti-tumor immunotherapy.

The dual role of calnexin on malignant progression and tumor microenvironment in glioma

Glioma is the most common malignant brain tumor. Previous studies have reported that calnexin (CANX) is significantly up-regulated in a variety of malignant tumors, including glioma, but its biological function and mechanism in glioma is still unclear. In this study, differentially expressed proteins in 3 primary glioblastoma multiforme (GBM) tissues and 3 paracancer tissues were identified by liquid chromatography-tandem mass spectrometry-based proteomic and bioinformatic analysis. The biological function and molecular mechanism of CANX were studied in glioma cell lines (T98G and A172) by CCK-8 assay, matrigel invasion assay, wound healing assay, flow cytometry and so on. Bioinformatics methods were used to analyze the immune microenvironment of glioma patients in TCGA database, and single cell sequencing data of 8 cases of untreated primary GBM in GEO database were analyzed. Proteomic analysis found that CANX was significantly overexpressed in glioma tissues comparing with paracancer tissues. The data from TCGA validated this result and showed that CANX was associated with poor prognosis of patients. A series of experiments at the cellular level found that CANX overexpression significantly enhanced the proliferation, migration and invasion ability of GBM cells, whereas CANX silencing had opposite effects. Further research found this effect may be mediated through the activation of the PI3K/AKT/mTOR signaling. In addition, immune infiltration analysis found that CANX high-expression glioma tissues exhibited fewer CD8+ T cells, natural killer cells and mast cells, along with significantly decreased tumor purity and significantly increased immune checkpoints expression. Single cell sequencing data analysis indicated that CANX was primarily expressed in astrocytes and dendritic cells. In conclusion, this study suggested that CANX may promote the malignant progression of glioma through PI3K/AKT/mTOR signaling pathway and play an important role in glioma immune escape. Therefore, CANX may be a valuable therapeutic target for glioma.

Necroptosis-based glioblastoma prognostic subtypes: implications for TME remodeling and therapy response

BACKGROUND

Glioblastoma (GBM) is an aggressive primary brain tumor with a high recurrence rate and poor prognosis. Necroptosis, a pathological hallmark of GBM, is poorly understood in terms of its role in prognosis, tumor microenvironment (TME) alteration, and immunotherapy.

METHODS & RESULTS

We assessed the expression of 55 necroptosis-related genes in GBM and normal brain tissues. We identified necroptosis-stratified clusters using Uni-Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression to establish the 10-gene Glioblastoma Necroptosis Index (GNI). GNI demonstrated significant prognostic efficacy in the TCGA dataset (n = 160) and internal validation dataset (n = 345) and in external validation cohorts (n = 591). The GNI-high subgroup displayed a mesenchymal phenotype, lacking the IDH1 mutation, and MGMT methylation. This subgroup was characterized by significant enrichment in inflammatory and humoral immune pathways with prominent cell adhesion molecules (CD44 and ICAM1), inflammatory cytokines (TGFB1, IL1B, and IL10), and chemokines (CX3CL1, CXCL9, and CCL5). The TME in this subgroup showed elevated infiltration of M0 macrophages, neutrophils, mast cells, and regulatory T cells. GNI-related genes appeared to limit macrophage polarization, as confirmed by immunohistochemistry and flow cytometry. The top 30% high-risk score subset exhibited increased CD8 T cell infiltration and enhanced cytolytic activity. GNI showed promise in predicting responses to immunotherapy and targeted treatment.

CONCLUSIONS

Our study highlights the role of necroptosis-related genes in glioblastoma (GBM) and their effects on the tumor microenvironment and patient prognosis. TheGNI demonstrates potential as a prognostic marker and provides insights into immune characteristics and treatment responsiveness.

TMEM35B as a novel biomarker for diagnosing gliomas

AIMS

There is little information about transmembrane protein 35B (TMEM35B) expression in glioma, and its functions in glioma remains no clue.

PATIENTS & METHODS

Immunohistochemistry was used to measure TMEM35B expression levels and CCK8 and Transwell assays were analyzed the proliferative and migratory and invasive.

RESULTS

TMEM35B protein was significantly higher in gliomas and correlated with a higher tumor TNM stages. Receiver operating characteristic curve analysis revealed that TMEM35B had high diagnostic value in distinguishing among glioma, normal tissues, tumor stages III+IV, and I+II. Additionally, TMEM35B knockdown inhibited the proliferative, migratory, and invasive capacities of glioma cells.

CONCLUSIONS

TMEM35B expression is upregulated in gliomas, and its knockdown hinders tumor progression, highlighting the protein as a potential biomarker for glioma diagnosis.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment

BACKGROUND

Gliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data.

METHODS

In this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted.

RESULTS

CD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram's predictions were supported by calibration plots, further validating its accuracy.

CONCLUSION

In conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Molecular subtypes based on immunologic and epithelial-mesenchymal transition gene sets reveal tumor immune microenvironment characteristics and implications for immunotherapy of patients with glioma

The tumor immune microenvironment (TIME) significantly influences cancer progression and treatment. This study sought to uncover novel TIME-related glioma biomarkers to advance antitumor immunotherapies by integrating data from sequencing of bulk RNA as well as scRNA. Immunologic and epithelial-mesenchymal transition (EMT) characteristics were used to classify glioma patients into two immune subtypes (ISs) and two EMT subtypes (ESs). Patients in IS1 and ES1, characterized by high immune infiltration and low stemness scores, exhibited poor clinical outcomes and limited responsiveness to immunotherapy. A new risk signature was developed using 16 genes and validated in independent glioma cohorts. Among these, HAVCR2, IL18, LAGLS9, and PTPN6 emerged as hub genes, with IL18 identified as a potential independent indicator. The upregulation of IL18 in high-grade gliomas and U-251 MG cells aligned with bioinformatics analysis. These insights deepen the understanding of TIME-related mechanisms in glioma and highlight potential therapeutic targets, offering a theoretical foundation for effective antitumor immunotherapies in glioma.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study

Introduction

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG.

Methods

Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model.

Results

Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability.

Discussion

Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Hypoxia-related THBD+ macrophages as a prognostic factor in glioma: Construction of a powerful risk model

Glioma is a prevalent malignant tumour characterized by hypoxia as a pivotal factor in its progression. This study aims to investigate the impact of the most severely hypoxic cell subpopulation in glioma. Our findings reveal that the THBD+ macrophage subpopulation is closely associated with hypoxia in glioma, exhibiting significantly higher infiltration in tumours compared to non-tumour tissues. Moreover, a high proportion of THBD+ cells correlates with poor prognosis in glioblastoma (GBM) patients. Notably, THBD+ macrophages exhibit hypoxic characteristics and epithelial-mesenchymal transition features. Silencing THBD expression leads to a notable reduction in the proliferation and metastasis of glioma cells. Furthermore, we developed a THBD+ macrophage-related risk signature (THBDMRS) through machine learning techniques. THBDMRS emerges as an independent prognostic factor for GBM patients with a substantial prognostic impact. By comparing THBDMRS with 119 established prognostic features, we demonstrate the superior prognostic performance of THBDMRS. Additionally, THBDMRS is associated with glioma metastasis and extracellular matrix remodelling. In conclusion, hypoxia-related THBD+ macrophages play a pivotal role in glioma pathogenesis, and THBDMRS emerges as a potent and promising prognostic tool for GBM, contributing to enhanced patient survival outcomes.

Construction of a New Immune-Related Competing Endogenous RNA Network with Prognostic Value in Lung Adenocarcinoma

Tumor microenvironment has significant influence on the gene expression of tumor tissues and on the clinical outcomes in lung adenocarcinoma. Infiltrating immune and stromal cells not only perturb the tumor signal in molecular studies, but also play crucial roles in cancer biology. The competing endogenous RNAs (ceRNAs) are useful to explain the post-transcriptional layer regulated by gene translation and play an important role in the occurrence and progression of lung adenocarcinoma. Therefore, identifying novel molecular markers by constructing ceRNA associated with immune infiltration is of great significance to guide the treatment of lung adenocarcinoma in the future. According to the immune and stromal scores of lung adenocarcinoma samples in The Cancer Genome Atlas (TCGA) database calculated by the ESTIMATE algorithm, we identified differentially expressed lncRNAs, miRNAs and mRNAs associated with immune infiltration, including 60 dysregulated lncRNAs, 38 dysregulated mRNAs, and 29 dysregulated miRNAs. Based on the PPI network and Cox regression analysis, 5 mRNAs including CNR2, P2RY12, ZNF831, RSPO1, and F2 were identified to be related to immune infiltration and prognosis in lung adenocarcinoma, and their differential expression, prognosis and correlation with immune cells were verified. Next, through target binding prediction, pearson correlation analysis and expression analysis, a novel immune-related ceRNA network containing 6 lncRNAs, 4 miRNAs, and 3 mRNAs was finally constructed. The present study constructed a novel immune-associated lncRNA-miRNA-mRNA ceRNA network, which deepens our understanding on the molecular network mechanism of lung adenocarcinoma and provides potential prognostic markers and novel therapeutic targets for the patients with lung adenocarcinoma.

Exploring the prognostic potential of m6A methylation regulators in low-grade glioma: implications for tumor microenvironment modulation

BACKGROUND

The biological behavior of low-grade glioma (LGG) is significantly affected by N6-methyladenosine (m6A) methylation, an essential epigenetic alteration. Therefore, it is crucial to create a prognostic model for LGG by utilizing genes that regulate m6A methylation.

METHODS

Using TCGA and GTEx databases. We examined m6A modulator levels in LGG and normal tissues, and investigated PD-L1 and PD-1 expression, immune scores, immune cell infiltration, tumor immune microenvironment (TIME) and potential underlying mechanisms in different LGG clusters. We also performed immunohistochemistry and RT-qPCR to identify essential m6A adjustment factor.

RESULTS

The results showed that m6A regulatory element expression was significantly increased in LGG tissues and was significantly associated with TMIE. A substantial increase in PD-L1 and PD-1 levels in LGG tissues and high-risk cohorts was observed. PD-L1 expression was positively correlated with FTO, ZCCHC4, and HNRNPD, whereas PD-1 expression was negatively correlated with FTO, ZC3H7B, and HNRNPD. The prognostic signature created using regulators of m6A RNA methylation was shown to be strongly associated with the overall survival of LGG patients, and FTO and ZCCHC4 were confirmed as independent prognostic markers by clinical samples. Furthermore, the results revealed different TIME characteristics between the two groups of patients, indicating disrupted signaling pathways associated with LGG.

CONCLUSION

Our results present that the m6A regulators play vital role in regulating PD-L1/PD-1 expression and the infiltration of immune cells, thereby exerting a sizable impact on the TIME of LGG. Therefore, m6A regulators have precise predictive value in the prognosis of LGG.

IGFBP5, as a Prognostic Indicator Promotes Tumor Progression and Correlates with Immune Microenvironment in Glioma

Background: Insulin-like growth factor binding protein 5 (IGFBP5) is highly expressed in multiple human cancers, including glioma. Despite this, it remains unclear what role it plays in glioma. The aim of the present study was to analyze whether IGFBP5 could be used as a predictor of prognosis and immune infiltration in glioma. Methods: Glioma patients' clinical information was collected from the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), Rembrandt, and Gravendeel databases. The diagnostic and prognostic roles of IGFBP5 were assessed by the Kaplan-Meier survival curves, diagnostic receiver operating characteristic (ROC) curves, nomogram model, Cox regression analysis and Enrichment analysis by R software. Moreover, the correlation between IGFBP5 expression and immune cell infiltration, and immune checkpoint genes was conducted. Immunohistochemistry staining, CCK8, colony formation, scratch and transwell assays and western blot were used to interrogate the expression and function of IGFBP5 in glioma. Results: IGFBP5 levels were obviously increased in glioma with higher malignancy and predicted poor outcomes by Univariate and multivariate Cox analysis. The biological function analysis revealed that IGFBP5 correlated closely with immune signatures. Moreover, IGFBP5 expression was associated with tumor infiltration of B cells, T cells, macrophages, and NK cells. IGFBP5 affected glioma cell proliferation, migration, and invasion probably involved in the epithelial-to-mesenchymal transition (EMT) and Hippo-YAP signaling pathway. Further study showed that IGFBP5 induced the expression of PD-L1 and CXCR4. Conclusions: IGFBP5 as an oncogene is a useful biomarker of prognosis and correlates with progression and immune infiltration in glioma.

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

An alternative extension of telomeres related prognostic model to predict survival in lower grade glioma

OBJECTIVE

The alternative extension of the telomeres (ALT) mechanism is activated in lower grade glioma (LGG), but the role of the ALT mechanism has not been well discussed. The primary purpose was to demonstrate the significance of the ALT mechanism in prognosis estimation for LGG patients.

METHOD

Gene expression and clinical data of LGG patients were collected from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) cohort, respectively. ALT-related genes obtained from the TelNet database and potential prognostic genes related to ALT were selected by LASSO regression to calculate an ALT-related risk score. Multivariate Cox regression analysis was performed to construct a prognosis signature, and a nomogram was used to represent this signature. Possible pathways of the ALT-related risk score are explored by enrichment analysis.

RESULT

The ALT-related risk score was calculated based on the LASSO regression coefficients of 22 genes and then divided into high-risk and low-risk groups according to the median. The ALT-related risk score is an independent predictor of LGG (HR and 95% CI in CGGA cohort: 5.70 (3.79, 8.58); in TCGA cohort: 1.96 (1.09, 3.54)). ROC analysis indicated that the model contained ALT-related risk score was superior to conventional clinical features (AUC: 0.818 vs 0.729) in CGGA cohorts. The results in the TCGA cohort also shown a powerful ability of ALT-related risk score (AUC: 0.766 vs 0.691). The predicted probability and actual probability of the nomogram are consistent. Enrichment analysis demonstrated that the ALT mechanism was involved in the cell cycle, DNA repair, immune processes, and others.

CONCLUSION

ALT-related risk score based on the 22-gene is an important factor in predicting the prognosis of LGG patients.

Development and validation of a personalized classifier to predict the prognosis and response to immunotherapy in glioma based on glycolysis and the tumor microenvironment

Background

Glycolysis is closely associated with cancer progression and treatment outcomes. However, the role of glycolysis in the immune microenvironment, prognosis, and immunotherapy of glioma remains unclear.

Methods

This study investigated the role of glycolysis on prognosis and its relationship with the tumor microenvironment (TME). Subsequently, we developed and validated the glycolysis-related gene signature (GRS)-TME classifier using multiple independent cohorts. Furthermore, we also examined the prognostic value, somatic alterations, molecular characteristics, and potential benefits of immunotherapy based on GRS-TME classifier. Lastly, the effect of kinesin family member 20A (KIF20A) on the proliferation and migration of glioma cells was evaluated in vitro.

Results

Glycolysis was identified as a significant prognostic risk factor in glioma, and closely associated with an immunosuppressive microenvironment characterized by altered distribution of immune cells. Furthermore, a personalized GRS-TME classifier was developed and validated by combining the glycolysis (18 genes) and TME (seven immune cells) scores. Patients in the GRSlow/TMEhigh subgroup exhibited a more favorable prognosis compared to other subgroups. Distinct genomic alterations and signaling pathways were observed among different subgroups, which are closely associated with cell cycle, epithelial-mesenchymal transition, p53 signaling pathway, and interferon-alpha response. Additionally, we found that patients in the GRSlow/TMEhigh subgroup exhibit a higher response rate to immunotherapy, and the GRS-TME classifier can serve as a novel biomarker for predicting immunotherapy outcomes. Finally, high expression of KIF20A is associated with an unfavorable prognosis in glioma, and its knockdown can inhibit the proliferation and migration of glioma cells.

Conclusions

Our study developed a GRS-TME classifier for predicting the prognosis and potential benefits of immunotherapy in glioma patients. Additionally, we identified KIF20A as a prognostic and therapeutic biomarker for glioma.

Novel immune checkpoint-related gene model to predict prognosis and treatment responsiveness in low-grade gliomas

Recently, studies have shown that immune checkpoint-related genes (ICGs) are instrumental in maintaining immune homeostasis and can be regarded as potential therapeutic targets. However, the prognostic applications of ICGs require further elucidation in low-grade glioma (LGG) cases. In the present study, a unique prognostic gene signature in LGG has been identified and validated as well based on ICGs as a means of facilitating clinical decision-making. The RNA-seq data as well as corresponding clinical data of LGG samples have been retrieved utilizing the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ICG-defined non-negative matrix factorization (NMF) clustering was performed to categorize patients with LGG into two molecular subtypes with different prognoses, clinical traits, and immune microenvironments. In the TCGA database, a signature integrating 8 genes has been developed utilizing the LASSO Cox method and validated in the GEO database. The signature developed is superior to other well-recognized signatures in terms of predicting the survival probability of patients with LGG. This 8-gene signature was then subsequently applied to categorize patients into high- and low-risk groups, and differences between them in terms of gene alteration frequency were observed. There were remarkable variations in IDH1 (91% and 64%) across low-as well as high-risk groups. Additionally, various analyses like function enrichment, tumor immune microenvironment, and chemotherapy drug sensitivity revealed significant variations across high- and low-risk populations. Overall, this 8-gene signature may function as a useful tool for prognosis and immunotherapy outcome predictions among LGG patients.

CCDC43 as a potential therapeutic target of Tian Yang Wan for the treatment of hepatocellular carcinoma by activating the hippo pathway

Introduction

Hepatocellular carcinoma (HCC) prevalence is rising annually, but the existing treatment strategies are limited; therefore, it is crucial to explore new therapeutic approaches.

Methods

Here, we investigate the potential anti-cancer mechanism of an herbal medicine called Tian Yang Wan (TYW) in the treatment of HCC. The relationship of CCDC43 with immunity and cell death was analyzed by bioinformatics. Confirming the tumor suppressor effect of TYW on HCC cells by proliferation, invasion, migration and apoptosis assays.

Results

First, we analyzed by proteomics that CCDC43 expression was downregulated after TYW administration and promoted the hippo pathway. Then, a large sample's transcriptome study demonstrated that elevated CCDC43 expression was strongly correlated with clinical traits and a bad prognosis in HCC patients. Next, we observed through multiple advanced algorithms that CCDC43 is involved in a variety of oncology and immunology related pathways. Notably, we found higher tumor immune microenvironment with high CCDC43 expression. Furthermore, we demonstrated that CCDC43 is associated with immune checkpoints and found that it is a sensitive indicator of a large number of chemotherapeutic agents. Subsequently, we conducted experimental investigations to demonstrate the capacity of TYW to impede proliferation and migration, while inducing apoptosis in human HCC cell lines. Finally, we performed analysis of two cell death patterns which showed CCDC43 to be strongly correlated with multiple ferroptosis factors and cuproptosis factors.

Discusion

In conclusion, our study comprehensively examined the prognostic, immunological, and therapeutic implications of CCDC43 in HCC, thereby elucidating the therapeutic mechanism of action in TYW.

Integrative Analysis of Inflammatory Response-Related Gene for Predicting Prognosis and Immunotherapy in Glioma

Inflammatory response plays a crucial role in the development and progression of gliomas. Whereas the prognostic esteem of inflammatory response-related genes has never been comprehensively explored in glioma, the RNA-seq information and clinical data of patients with glioma were extracted from TCGA, CGGA, and Rembrandt databases. The differentially expressed genes (DEGs) were picked out between glioma tissue and non-tumor brain tissue (NBT). Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct the prognostic signature in the TCGA cohort and verified in other cohorts. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) between the high and low-risk groups. Univariate and multivariate Cox analyses were subsequently used to confirm the independent prognostic factors of OS, and then, the nomogram was established based them. Furthermore, immune infiltration, immune checkpoints, and immunotherapy were also probed and compared between high and low-risk groups. The four genes were also analyzed by qRT-PCR, immunohistochemistry, and western blot trials between glioma tissue and NBT. The 39 DEGs were identified between glioma tissue and NBT, of which 31 genes are associated to the prognosis of glioma. The 8 optimal inflammatory response-related genes were selected to construct the prognostic inflammatory response-related signature (IRRS) through the LASSO regression. The effectiveness of the IRRS was verified in the TCGA, CGGA, and Rembrandt cohorts. Meanwhile, a nomogram with better accuracy was established to predict OS based on the independent prognostic factors. The IRRS was highly correlated with clinicopathological features, immune infiltration, and genomic alterations in glioma patients. In addition, four selective genes also verified the difference between glioma tissue and NBT. A novel prognostic signature was associated with the prognosis, immune infiltration, and immunotherapy effect in patients with gliomas. Thus, this study could provide a perspective for glioma prognosis and treatment.

Identification of Genes Associated with Prognosis and Immunotherapy Prediction in Triple-Negative Breast Cancer via M1/M2 Macrophage Ratio

Background and Objectives: Triple-negative breast cancer (TNBC), a highly aggressive and heterogeneous subtype of breast cancer, accounts for ap-proximately 10-15% of all breast cancer cases. Currently, there is no effective therapeutic target for TNBC. Tu-mor-associated macrophages (TAMs), which can be phenotypically classified into M1 and M2 subtypes, have been shown to influence the prognosis of various cancers, including ovarian cancer. This study aimed to investigate the role of M1/M2 macrophages in the TNBC tumor microenvironment (TME), with a focus on identifying prognostic genes and predicting immunotherapy response. Materials and Methods: The study employed the CIBERSORT algorithm to analyze immune cell expression in the TME. Genes associated with the M1/M2 macrophage ratio were identified using Pearson correlation analysis and used to classify patients into dis-tinct clusters. Dimensionality reduction techniques, including univariate Cox regression and Lasso, were applied to these genes. The expression of prognostic genes was validated through immunohistochemistry. Results: The study found a high prevalence of TAMs in the TME. Among the patient clusters, 109 differentially expressed genes (DEGs) were identified. Three significant DEGs (LAMP3, GZMB, and CXCL13) were used to construct the riskScores. The riskScore model effectively stratified patients based on mortality risk. Gene Set Enrichment Analysis (GSEA) associated the riskScore with several significant pathways, including mismatch repair, JAK/STAT3 signaling, VEGF signaling, antigen processing presentation, ERBB signaling, and P53 signaling. The study also predicted patient sensitivity to im-munotherapy using the riskScores. The expression of the three significant DEGs was validated through immunohisto-chemistry. Conclusions: The study concluded that the riskScore model, based on the M1/M2 macrophage ratio, is a valid prognostic tool for TNBC. The findings underscore the importance of the TME in TNBC progression and prognosis and highlight the po-tential of the riskScore model in predicting immunotherapy response in TNBC patients.

Construction of a novel nomogram based on competing endogenous RNAs and tumor-infiltrating immune cells for prognosis prediction in elderly patients with colorectal cancer

Competitive endogenous RNAs (ceRNAs) and tumor-infiltrating immune cells play essential roles in colorectal cancer (CRC) tumorigenesis. However, their prognostic role in elderly patients with CRC is unclear. Gene expression profiles and clinical information for elderly patients with CRC were downloaded from The Cancer Genome Atlas. Univariate, LASSO, and multivariate Cox regression analyses were utilized for screening key ceRNAs and prevent overfitting. A total of 265 elderly patients with CRC were included. We constructed a novel ceRNA network consisting of 17 lncRNAs, 35 miRNAs, and 5 mRNAs. We established three prognosis predictive nomograms based on four key ceRNAs (ceRNA nomogram), five key immune cells (immune cell nomogram), and their combination (ceRNA-immune cell nomogram). Among them, the ceRNA-immune cell nomogram had the best accuracy. Furthermore, the areas under the curve of the ceRNA-immune cell nomogram were also significantly greater than the TNM stage at 1 (0.818 vs. 0.693), 3 (0.865 vs. 0.674), and 5 (0.832 vs. 0.627) years. Co-expression analysis revealed that CBX6 was positively correlated with activated dendritic cells (R = 0.45, p < 0.01), whereas negatively correlated with activated mast cells (R =- 0.43, p < 0.01). In conclusion, our study constructed three nomograms to predict prognosis in elderly patients with CRC, among which the ceRNA-immune cell nomogram had the best prediction accuracy. We inferred that the mechanism underlying the regulation of activated dendritic cells and mast cells by CBX6 might play a crucial role in tumor development and prognosis of elderly patients with CRC.

The prognostic value and immune landscaps of m6A/m5C-related lncRNAs signature in the low grade glioma

BACKGROUND

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) are the main RNA methylation modifications involved in the oncogenesis of cancer. However, it remains obscure whether m6A/m5C-related long non-coding RNAs (lncRNAs) affect the development and progression of low grade gliomas (LGG).

METHODS

We summarized 926 LGG tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. 105 normal brain samples with RNA-seq data from the Genotype Tissue Expression project were collected for control. We obtained a molecular classification cluster from the expression pattern of sreened lncRNAs. The least absolute shrinkage and selection operator Cox regression was employed to construct a m6A/m5C-related lncRNAs prognostic signature of LGG. In vitro experiments were employed to validate the biological functions of lncRNAs in our risk model.

RESULTS

The expression pattern of 14 sreened highly correlated lncRNAs could cluster samples into two groups, in which various clinicopathological features and the tumor immune microenvironment were significantly distinct. The survival time of cluster 1 was significantly reduced compared with cluster 2. This prognostic signature is based on 8 m6A/m5C-related lncRNAs (GDNF-AS1, HOXA-AS3, LINC00346, LINC00664, LINC00665, MIR155HG, NEAT1, RHPN1-AS1). Patients in the high-risk group harbored shorter survival times. Immunity microenvironment analysis showed B cells, CD4 + T cells, macrophages, and myeloid-derived DC cells were significantly increased in the high-risk group. Patients in high-risk group had the worse overall survival time regardless of followed TMZ therapy or radiotherapy. All observed results from the TCGA-LGG cohort could be validated in CGGA cohort. Afterwards, LINC00664 was found to promote cell viability, invasion and migration ability of glioma cells in vitro.

CONCLUSION

Our study elucidated a prognostic prediction model of LGG by 8 m6A/m5C methylated lncRNAs and a critical lncRNA regulation function involved in LGG progression. High-risk patients have shorter survival times and a pro-tumor immune microenvironment.

FDX1 serves as a prognostic biomarker and promotes glioma progression by regulating the immune response

The present study investigates the prognostic value of the FDX1 gene and its association with immune infiltration in gliomas. Gene expression profiles and corresponding clinical parameters of glioma patients were obtained from the Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. In vitro experiments were also performed to validate its impact on malignant phenotypes of glioma cells. Kaplan-Meier analysis demonstrated that high FDX1 expression was associated with poor prognosis in glioma. Function and pathway enrichment for FDX1 predominantly demonstrated immunomodulatory function. In addition, the high-FDX1 expression group had higher Estimation of Stromal and Immune cells in malignant tumor tissues using Expression data, stromal, and immune scores (p<0.001). On evaluation of immunotherapy response, TIDE and dysfunction scores were higher in the low-FDX1 group, while the exclusion score demonstrated an opposite trend. In vitro tests showed that FDX1 silencing-induced inhibition of cell invasion and migration inactivated the nucleotide oligomerization domain (NOD)-like receptor signaling pathway by regulating PD-L1 expression. Notably, NOD1 expression was reversed in FDX1-knockdown cells after treatment with NOD1 agonists. In conclusion, FDX1 may play an important role in the diagnosis and treatment of gliomas. Regulating its expression may therefore help improve immunotherapy for these tumors.

The Oncogenic Role of Cyclin-Dependent Kinase Inhibitor 2C in Lower-Grade Glioma

Lower-grade gliomas (LGGs) are slow-growing, indolent tumors that usually affect younger patients and present a therapeutic challenge due to the heterogeneity of their clinical presentation. Dysregulation of cell cycle regulatory factors is implicated in the progression of many tumors, and drugs that target cell cycle machinery have shown efficacy as promising therapeutic approaches. To date, however, no comprehensive study has examined how cell cycle-related genes affect LGG outcomes. The cancer genome atlas (TCGA) data were used as the training set for differential analysis of gene expression and patient outcomes; the Chinese glioma genome atlas (CGGA) was used for validation. Levels of one candidate protein, cyclin-dependent kinase inhibitor 2C (CDKN2C), and its relationship to clinical prognosis were determined using a tissue microarray containing 34 LGG tumors. A nomogram was constructed to model the putative role of candidate factors in LGG. Cell type proportion analysis was performed to evaluate immune cell infiltration in LGG. Various genes encoding cell cycle regulatory factors showed increased expression in LGG and were significantly related to isocitrate dehydrogenase and chromosome arms 1p and 19q mutation status. CDKN2C expression independently predicted the outcome of LGG patients. High M2 macrophage values along with elevated CDKN2C expression were associated with poorer prognosis in LGG patients. CDKN2C plays an oncogenic role in LGG, which is associated with M2 macrophages.

Integrative transcriptomics analysis and experimental validation reveal immunomodulatory patterns in keratoconus

Keratoconus is a progressive disorder of the cornea and is typically considered a noninflammatory disease. However, increasing evidence indicates that immune disorders play an essential role in keratoconus progression, but the immune-related etiology remains elusive. Here, we comprehensively utilized bioinformatics approaches and experimental methods to explore the potential immunoregulatory mechanism of keratoconus progression. Transcriptomics data containing two keratoconus patient groups was derived from the public dataset GSE151631. The intersection of genes and known immunological genes was used to obtain differentially expressed immune-related genes. We utilized various protein clustering algorithms to screen out and validated the hub immune-related genes, and further explored their potential biological functions via gene annotation and pathway enrichment analyses. Moreover, the underlying immune landscape and drug targets were predicted by immune cell infiltration analysis and drug-gene interaction analysis. Furthermore, keratoconus-related immunoregulatory competitive endogenous RNA networks were constructed and experimentally validated. After filtering and experimental validation, nine keratoconus-associated immune-related genes were credible. Infiltrated monocytes might play an essential role in the progression of keratoconus. Moreover, eleven intersecting drugs targeting four genes, CCR2, CCR5, F2RL1, and ADORA1, were considered as potential druggable molecular targets for keratoconus. Furthermore, in the competitive endogenous RNA network, we identified several lncRNAs and miRNAs as critical noncoding RNAs regulating the hub genes. Overall, our data indicated that the immunomodulatory patterns had undergone changes in the pathogenesis of keratoconus, which might facilitate the understanding of keratoconus-related immune processes and provide novel insights into developing new immunotherapies for keratoconus.

Recognition of Differentially Expressed Molecular Signatures and Pathways Associated with COVID-19 Poor Prognosis in Glioblastoma Patients

Glioblastoma (GBM) is a type of brain cancer that is typically very aggressive and difficult to treat. Glioblastoma cases have been reported to have increased during COVID-19. The mechanisms underlying this comorbidity, including genomic interactions, tumor differentiation, immune responses, and host defense, are not completely explained. Therefore, we intended to investigate the differentially expressed shared genes and therapeutic agents which are significant for these conditions by using in silico approaches. Gene expression datasets of GSE68848, GSE169158, and GSE4290 studies were collected and analyzed to identify the DEGs between the diseased and the control samples. Then, the ontology of the genes and the metabolic pathway enrichment analysis were carried out for the classified samples based on expression values. Protein-protein interactions (PPI) map were performed by STRING and fine-tuned by Cytoscape to screen the enriched gene module. In addition, the connectivity map was used for the prediction of potential drugs. As a result, 154 overexpressed and 234 under-expressed genes were identified as common DEGs. These genes were found to be significantly enriched in the pathways involved in viral diseases, NOD-like receptor signaling pathway, the cGMP-PKG signaling pathway, growth hormone synthesis, secretion, and action, the immune system, interferon signaling, and the neuronal system. STAT1, CXCL10, and SAMDL were screened out as the top 03 out of the top 10 most critical genes among the DEGs from the PPI network. AZD-8055, methotrexate, and ruxolitinib were predicted to be the possible agents for the treatment. The current study identified significant key genes, common metabolic signaling networks, and therapeutic agents to improve our perception of the common mechanisms of GBM-COVID-19.

Neuroimmune transcriptome changes in patient brains of psychiatric and neurological disorders

Neuroinflammation has been implicated in multiple brain disorders but the extent and the magnitude of change in immune-related genes (IRGs) across distinct brain disorders has not been directly compared. In this study, 1275 IRGs were curated and their expression changes investigated in 2467 postmortem brains of controls and patients with six major brain disorders, including schizophrenia (SCZ), bipolar disorder (BD), autism spectrum disorder (ASD), major depressive disorder (MDD), Alzheimer's disease (AD), and Parkinson's disease (PD). There were 865 IRGs present across all microarray and RNA-seq datasets. More than 60% of the IRGs had significantly altered expression in at least one of the six disorders. The differentially expressed immune-related genes (dIRGs) shared across disorders were mainly related to innate immunity. Moreover, sex, tissue, and putative cell type were systematically evaluated for immune alterations in different neuropsychiatric disorders. Co-expression networks revealed that transcripts of the neuroimmune systems interacted with neuronal-systems, both of which contribute to the pathology of brain disorders. However, only a few genes with expression changes were also identified as containing risk variants in genome-wide association studies. The transcriptome alterations at gene and network levels may clarify the immune-related pathophysiology and help to better define neuropsychiatric and neurological disorders.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning

Background

Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.

Methods

A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group.

Results

The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644.

Conclusion

We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na⁺-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

PSMC6 induces immune cell infiltration and inflammatory response to aggravate primary Sjögren's syndrome

Increasing evidence suggests that immune cell infiltration is involved in primary Sjögren's syndrome (pSS), while the underlying molecular mechanisms remain elusive. Herein, this study aims to explore the key molecular mechanism in immune cell infiltration in pSS based on Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained, followed by weighted gene co-expression network analysis to acquire the pSS-related module genes. Moreover, pSS-related DEGs and module genes were intersected. Additionally, the correlation between key genes and immune cell infiltration was analyzed by CIBERSORT algorithm. Furthermore, pSS mouse models were established to explore the effects of PSMC6 on immune cell infiltration and inflammatory responses in pSS. A total of 51 DEGs and 334 key module genes were involved in the occurrence of pSS. The immune cell infiltration was correlated with pSS, and PSMC6, highly expressed in pSS samples, may be the key immune gene. In vivo animal experiments demonstrated that PSMC6 was upregulated in pSS, and PSMC6 knockdown could reduce lymphocytic infiltration in salivary glands and lacrimal glands and the levels of related inflammatory factors in the pSS and increase the proportion of Treg cells. Collectively, PSMC6 could induce immune cell infiltration and inflammatory responses to promote the occurrence of pSS, providing us with a potential therapeutic target for treating pSS.

Identification and validation of RNA methylation-related alternative splicing gene signature for low-grade glioma to predict survival and immune landscapes

BACKGROUND

Low-grade glioma (LGG) is a crucial pathological type of glioma. The present study aimed to explore multiple RNA methylation regulator-related AS events and investigate their prognostic values in LGG.

METHODS

The prognostic model for low-grade glioma was established using the LASSO regression analysis. To validate prognostic value, we performed Kaplan-Maier survival analysis, ROC curves and nomograms. The ESTIMATE algorithm, the CIBERSORT algorithm and the ssGSEA algorithm were utilized to explore the role of the immune microenvironment in LGG. Subsequently, we then used GO, KEGG and GSEA enrichment analysis to explore the functional roles of these genes. In addition, we employed the GDSC database to screen potential chemotherapeutic agents.

RESULTS

Eight RNA methylation related AS events were involved in construct a survival and prognosis model, which had good ability of independent prediction for patients with LGG. Patients in the high-risk group had shorter life expectancy and higher mortality, while patients in the low-risk group had a better prognosis. We constructed a nomogram which showed an excellent predictive performance for individual OS. The risk score exhibited a close correlation with some immune cells and expression of immune checkpoints. Patients in high-risk group were characterized by immunosuppressive microenvironment and poor response to immunotherapy, and were sensitive to more chemotherapeutic drugs. Pathway and functional enrichment analyses further confirmed that significant differences existed in immune landscape between the two subgroups.

CONCLUSION

The prognostic RNA methylation-related alternative splicing signature constructed could constitute a promising prognostic biomarker, which could serve to optimize treatment regimens.

Cuproptosis-associated genes and immune microenvironment characterization in breast cancer

Excess Cu can cause cell death as a cofactor for essential enzymes. The relationship between cuproptosis-associated genes (CAGs) and breast cancer (BR) is not completely investigated. Here, the transcriptome expression and mutation profile data of BR samples from the Cancer Genome Atlas database were retrieved to identify CAGs. Patients with BR were clustered using consensus clustering. A least absolute shrinkage and selection operator analysis was then performed to construct a CAGs risk signature. As a result, all 13 cuproptosis regulators were significantly differentially expressed between BR and normal samples; among them, 9 cuproptosis genes were correlated with prognoses. Patients with BR were separated into 2 clusters that were associated with patient survival, clinical phenotypes, and immune infiltration, Based on the components of cuproptosis. Subsequently, genes differentially expressed between clusters were obtained, and 11 CAGs were ultimately incorporated into the risk signature. Functional analyses revealed that the risk signature correlated with patient outcomes, ER, PR, HER2 expression, and BR IHC subtypes. Additionally, immune microenvironment analyses showed that CAGs-high-risk patients exhibited lower immune cell infiltration and immune functions. Furthermore, high-risk BR patients had higher TMB, lower immune checkpoint expression, higher m6A gene expression, and higher tumor stemness. Finally, the immunophenoscore analysis revealed that the risk signature could potentially predict the immune response in BR and help guide the application of various immunotherapeutic drugs. Overall, the newly constructed CAGs risk signature presented a predictive value for the prognosis and tumor microenvironment of BR patients and can be further used in the guidance of immunotherapy for BR.

Identification of prognosis-related gene features in low-grade glioma based on ssGSEA

Low-grade gliomas (LGG) are commonly seen in clinical practice, and the prognosis is often poor. Therefore, the determination of immune-related risk scores and immune-related targets for predicting prognoses in patients with LGG is crucial. A single-sample gene set enrichment analysis (ssGSEA) was performed on 22 immune gene sets to calculate immune-based prognostic scores. The prognostic value of the 22 immune cells for predicting overall survival (OS) was assessed using the least absolute shrinkage and selection operator (LASSO) and univariate and multivariate Cox analyses. Subsequently, we constructed a validated effector T-cell risk score (TCRS) to identify the immune subtypes and inflammatory immune features of LGG patients. We divided an LGG patient into a high-risk-score group and a low-risk-score group based on the optimal cutoff value. Kaplan-Meier survival curve showed that patients in the low-risk-score group had higher OS. We then identified the differentially expressed genes (DEGs) between the high-risk-score group and low-risk-score group and obtained 799 upregulated genes and 348 downregulated genes. The analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) show that DEGs were mainly concentrated in immune-related processes. In order to further explore the immune-related genes related to prognosis, we constructed a protein-protein interaction (PPI) network using Cytoscape and then identified the 50 most crucial genes. Subsequently, nine DEGs were found to be significantly associated with OS based on univariate and multivariate Cox analyses. It was further confirmed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were independent prognostic factors for LGG through batch survival analysis and a nomogram prediction model. In addition, we used an RT-qPCR assay to validate the bioinformatics results. The results showed that CD2, SPN, IL18, PTPRC, GZMA, and TLR7 were highly expressed in LGG. Our study can provide a reference value for the prediction of prognosis in LGG patients and may help in the clinical development of effective therapeutic agents.

Worldwide productivity and research trend of publications concerning glioma-associated macrophage/microglia: A bibliometric study

Glioma-associated macrophage/microglia (GAM) represents a key player in shaping a unique glioma ecosystem to facilitate tumor progression and therapeutic resistance. Numerous studies have been published concerning GAM, but no relevant bibliometric study has been performed yet. Our bibliometric study aimed to comprehensively summarize and analyze the global scientific output, research hotspots, and trendy topics of publications on GAM over time. Data on publications on GAM were collected using the Web of Science (WoS). The search date was 16 January 2022, and the publications were collected from 2002 to 2021. Totally, 1,224 articles and reviews were incorporated and analyzed in the current study. It showed that the annual publications concerning GAM kept increasing over the past 20 years. The United States had the largest number of publications and total citations. Holland, Kettenmann, and Gutmann were the top three authors in terms of citation frequency. Neuro-oncology represented the most influential journal in GAM studies, with the highest H-index, total citations, and publication numbers. The paper published by Hambardzumyan in 2016 had the highest local citations. Additionally, the analysis of keywords implied that "prognosis," "tumor microenvironment," and "immunotherapy" might become research hotspots. Furthermore, trendy topics in GAM studies suggested that "immune infiltration," "immune microenvironment," "bioinformatics," "prognosis," and "immunotherapy" deserved additional attention. In conclusion, this bibliometric study comprehensively analyzed the publication trend of GAM studies for the past 20 years, in which the research hotspots and trendy topics were also uncovered. This information offered scholars critical references for conducting in-depth studies on GAM in the future.

Exploring the Correlation of Abnormal CXCL9 Expression with Immune Infiltration in Glioma and Patient Prognosis Based on TCGA and GTEx Databases

In this study, we intend to identify key immune-related genes (IRGs) in gliomas using the TCGA and GTEx databases. Following collection of the RNA-seq data of lower-grade glioma (LGG) and glioblastoma (GBM) patients from the TCGA and GTEx databases, the differentially expressed IRGs (DE-IRGs) were screened. The ESTIMATE algorithm was utilized to evaluate StromalScore and ImmuneScore of LGG and GBM samples and a multifactorial Cox risk model was constructed to identify the related risk genes. The core IRGs of LGG and GBM were screened through a PPI network, followed by exploration of their correlation with glioma prognosis. The relationship between IRGs and immune cells in LGG and GBM was detected. In vitro assays were performed to detect the effect of CXCL9 on glioma cell development. We screened 403 and 492 DE-IRGs in LGG and GBM. StromalScore and ImmuneScore were related to overall survival in LGG, but not in GBM. CXCL9 was identified as a core gene in LGG and GBM and shared association with the prognosis of gliomas. Furthermore, a correlation was found between CXCL9 and immune infiltration of LGG and GBM. Glioma cell proliferation and invasion could be inhibited by silencing of CXCL9. Overall, CXCL9 is correlated to the prognosis of glioma patients and may accelerate glioma development via immune regulation.

Molecular subtypes of cuproptosis regulators and their correlation with clinical prognosis and immune response in glioma

Cuproptosis is a newly described form of cell death. However, nothing is known about the roles of cuproptosis regulators in glioma. First, we explored the characteristics of cuproptosis molecular subtypes and relevant tumor microenvironment (TME) immune cell infiltration patterns in glioma. Using unsupervised clustering analysis, we identified two cuproptosis subtypes and three gene clusters that exhibited different clinical characteristics and TME cell infiltration patterns. Then, we developed and validated a cuproptosis-related prognostic model for predicting the overall survival of glioma patients. We established a risk score tool based on a nomogram to assess the clinical applicability of the cuproptosis model. A high cuproptosis risk score with high immune cell infiltration level, tumor mutation burden, gene alterations, and immunity activation had an unfavorable overall survival. Next, we identified possible competing endogenous ribonucleic acid regulatory networks based on significantly differentially expressed genes between high-risk and low-risk groups and screened several candidate small molecular compounds that may improve chemotherapy. Data from IMvigor and GSE78200 showed that the cuproptosis score affected the prognosis of patients who received immunotherapy. Our study indicated that cuproptosis regulators are involved in TME immune infiltration and impact the clinical prognosis in glioma. It is necessary for clinical practice to develop different therapeutic strategies according to the different phenotypes associated with immune response. The present findings provide new insight for improving immunotherapy strategies and individualized treatment in glioma.

TLR3 serves as a novel diagnostic and prognostic biomarker and is closely correlated with immune microenvironment in three types of cancer

Background: Toll-like receptor 3 (TLR3) plays an important role in both innate and adaptive immunity, but the prognostic value of TLR3 in heterogeneous tumors and the correlations between TLR3 expression and immune infiltration of heterogeneous tumors remain unclear. Methods: We investigated the expression of TLR3 in a variety of tumors and focused on the diagnostic and prognostic values of TLR3 in kidney renal clear cell carcinoma (KIRC), pancreatic adenocarcinoma (PAAD) and brain lower grade glioma (LGG) by GEPIA, DriverDBv3, UALCAN, TIMER, LinkedOmics, STRING, GeneMANIA and FunRich, as well as the possible mechanisms of TLR3 affecting tumor prognosis were discussed. Additionally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to validate TLR3 expression in early KIRC. We also compared the expression of TLR3 in the plasma of early KIRC patients and normal controls by enzyme linked immunosorbent assay (ELISA). Results: TLR3 expression was significantly different in multiple tumors compared with paracancerous nontumor tissues. Elevated expression of TLR3 contributed to the prolonged survival outcome in KIRC patients. Suppressed expression of TLR3 contributed to the prolonged survival outcome in LGG and PAAD patients. Moreover, TLR3 was significantly elevated in stage1, grade1 and N0 of KIRC. The expression and function of TLR3 in KIRC, LGG and PAAD were closely related to tumor immune microenvironment. TRAF6 was a key gene in the interactions between TLR3 and its interacting genes. Finally, the results of RT-qPCR and ELISA indicated that TLR3 expression levels were significantly raised in renal tissue and plasma of early KIRC patients. Conclusion: TLR3 has the potential to be a diagnostic biomarker of KIRC, LGG and PAAD as well as a biomarker for evaluating the prognosis of KIRC, LGG and PAAD, particularly for the early diagnosis of KIRC. TLR3 affects tumors mainly by acting on the immune microenvironment of KIRC, LGG and PAAD. These findings could lead to new insights into the immunotherapeutic targets for KIRC, LGG, and PAAD.

Comprehensive Analysis of a Novel Immune-Related Gene Signature in Lung Adenocarcinoma

Lung cancer is the major cause of cancer-related deaths around the world. Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, contributed to the majority of mortalities and showed different clinical outcomes in prognosis. Tumor-infiltrated immune cells at the tumor site are associated with better survival and immunotherapy response. Thus, it is essential to further investigate the molecular mechanisms and new prognostic biomarkers of lung adenocarcinoma development and progression. In this study, a six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was established to predict the prognosis of LUAD patients, as well as predictive value. The prognostic risk model was also significantly associated with the infiltration of immune cells in LUAD microenvironments. To sum up, a novel immune-related six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was identified that could predict LUAD survival and is highly related to B cells and dendritic cells, which may provide a theoretical basis of personalized treatment for targeted immunotherapy.

Development of a Hallmark Pathway-Related Gene Signature Associated with Immune Response for Lower Grade Gliomas

Although some biomarkers have been used to predict prognosis of lower-grade gliomas (LGGs), a pathway-related signature associated with immune response has not been developed. A key signaling pathway was determined according to the lowest adjusted p value among 50 hallmark pathways. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox analyses were performed to construct a pathway-related gene signature. Somatic mutation, drug sensitivity and prediction of immunotherapy analyses were conducted to reveal the value of this signature in targeted therapies. In this study, an allograft rejection (AR) pathway was considered as a crucial signaling pathway, and we constructed an AR-related five-gene signature, which can independently predict the prognosis of LGGs. High-AR LGG patients had higher tumor mutation burden (TMB), Immunophenscore (IPS), IMmuno-PREdictive Score (IMPRES), T cell-inflamed gene expression profile (GEP) score and MHC I association immunoscore (MIAS) than low-AR patients. Most importantly, our signature can be validated in four immunotherapy cohorts. Furthermore, IC50 values of the six classic chemotherapeutic drugs were significantly elevated in the low-AR group compared with the high-AR group. This signature might be regarded as an underlying biomarker in predicting prognosis for LGGs, possibly providing more therapeutic strategies for future clinical research.

TP53I13 promotes metastasis in glioma via macrophages, neutrophils, and fibroblasts and is a potential prognostic biomarker

Background

TP53I13 is a protein coding tumor suppression gene encoded by the tumor protein p53. Overexpression of TP53I13 impedes tumor cell proliferation. Nevertheless, TP53I13 role and expression in the emergence and progression of glioma (low-grade glioma and glioblastoma) are yet to be identified. Thus, we aim to use comprehensive bioinformatics analyses to investigate TP53I13 and its prognostic value in gliomas.

Methods

Multiple databases were consulted to evaluate and assess the expression of TP53I13, such as the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GeneMANIA, and Gene Expression Profiling Interactive. TP53I13 expression was further explored using immunohistochemistry (IHC) and multiplex immunohistochemistry (mIHC). Through Gene Set Enrichment Analysis (GSEA), the biological functions of TP53I13 and metastatic processes associated with it were studied.

Results

The expression of TP53I13 was higher in tumor samples compared to normal samples. In samples retrieved from the TCGA and CGGA databases, high TP53I13 expression was associated with poor survival outcomes. The analysis of multivariate Cox showed that TP53I13 might be an independent prognostic marker of glioma. It was also found that increased expression of TP53I13 was significantly correlated with PRS type, status, 1p/19q codeletion status, IDH mutation status, chemotherapy, age, and tumor grade. According to CIBERSORT (Cell-type Identification by Estimating Relative Subsets of RNA Transcript), the expression of TP53I13 correlates with macrophages, neutrophils, and dendritic cells. GSEA shows a close correlation between TP53I13 and p53 signaling pathways, DNA replication, and the pentose phosphate pathway.

Conclusion

Our results reveal a close correlation between TP53I13 and gliomas. Further, TP53I13 expression could affect the survival outcomes in glioma patients. In addition, TP53I13 was an independent marker that was crucial in regulating the infiltration of immune cells into tumors. As a result of these findings, TP53I13 might represent a new biomarker of immune infiltration and prognosis in patients with gliomas.

A novel risk score model based on fourteen chromatin regulators-based genes for predicting overall survival of patients with lower-grade gliomas

Background: Low grade gliomas(LGGs) present vexatious management issues for neurosurgeons. Chromatin regulators (CRs) are emerging as a focus of tumor research due to their pivotal role in tumorigenesis and progression. Hence, the goal of the current work was to unveil the function and value of CRs in patients with LGGs.

Methods: RNA-Sequencing and corresponding clinical data were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. A single-cell RNA-seq dataset was sourced from the Gene Expression Omnibus (GEO) database. Altogether 870 CRs were retrieved from the published articles in top academic journals. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were applied to construct the prognostic risk model. Patients were then assigned into high- and low-risk groups based on the median risk score. The Kaplan–Meier (K-M) survival curve and receiver operating characteristic curve (ROC) were performed to assess the prognostic value. Sequentially, functional enrichment, tumor immune microenvironment, tumor mutation burden, drug prediction, single cell analysis and so on were analyzed to further explore the value of CR-based signature. Finally, the expression of signature genes were validated by immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR).

Results: We successfully constructed and validated a 14 CRs-based model for predicting the prognosis of patients with LGGs. Moreover, we also found 14 CRs-based model was an independent prognostic factor. Functional analysis revealed that the differentially expressed genes were mainly enriched in tumor and immune related pathways. Subsequently, our research uncovered that LGGs patients with higher risk scores exhibited a higher TMB and were less likely to be responsive to immunotherapy. Meanwhile, the results of drug analysis offered several potential drug candidates. Furthermore, tSNE plots highlighting the magnitude of expression of the genes of interest in the cells from the scRNA-seq assay. Ultimately, transcription expression of six representative signature genes at the mRNA level was consistent with their protein expression changes.

Conclusion: Our findings provided a reliable biomarker for predicting the prognosis, which is expected to offer new insight into LGGs management and would hopefully become a promising target for future research.

Machine learning identification of cuproptosis and necroptosis-associated molecular subtypes to aid in prognosis assessment and immunotherapy response prediction in low-grade glioma

Both cuproptosis and necroptosis are typical cell death processes that serve essential regulatory roles in the onset and progression of malignancies, including low-grade glioma (LGG). Nonetheless, there remains a paucity of research on cuproptosis and necroptosis-related gene (CNRG) prognostic signature in patients with LGG. We acquired patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and captured CNRGs from the well-recognized literature. Firstly, we comprehensively summarized the pan-cancer landscape of CNRGs from the perspective of expression traits, prognostic values, mutation profiles, and pathway regulation. Then, we devised a technique for predicting the clinical efficacy of immunotherapy for LGG patients. Non-negative matrix factorization (NMF) defined by CNRGs with prognostic values was performed to generate molecular subtypes (i.e., C1 and C2). C1 subtype is characterized by poor prognosis in terms of disease-specific survival (DSS), progression-free survival (PFS), and overall survival (OS), more patients with G3 and tumour recurrence, high abundance of immunocyte infiltration, high expression of immune checkpoints, and poor response to immunotherapy. LASSO-SVM-random Forest analysis was performed to aid in developing a novel and robust CNRG-based prognostic signature. LGG patients in the TCGA and GEO databases were categorized into the training and test cohorts, respectively. A five-gene signature, including SQSTM1, ZBP1, PLK1, CFLAR, and FADD, for predicting OS of LGG patients was constructed and its predictive reliability was confirmed in both training and test cohorts. In both the training and the test datasets (cohorts), higher risk scores were linked to a lower OS rate. The time-dependent ROC curve proved that the risk score had outstanding prediction efficiency for LGG patients in the training and test cohorts. Univariate and multivariate Cox regression analyses showed the CNRG-based prognostic signature independently functioned as a risk factor for OS in LGG patients. Furthermore, we developed a highly reliable nomogram to facilitate the clinical practice of the CNRG-based prognostic signature (AUC > 0.9). Collectively, our results gave a promising understanding of cuproptosis and necroptosis in LGG, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients.

Identification of an endoplasmic reticulum stress-associated gene signature to predict the immune status and prognosis of cutaneous melanoma

Besides protecting normal cells from various internal and external perturbations, endoplasmic reticulum (ER) stress is also directly related to the pathogenesis of cutaneous melanoma (CM). However, due to the lack of specific molecular biomarkers, ER stress has not been considered a novel treatment target for CM. Here, we identified ER stress-related genes involved in the prognosis of CM patients and constructed an effective model for the prognostic prediction of these patients. First, gene expression data of CM and normal skin tissues from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases were retrieved to identify differentially expressed ER stress-related genes in CM. Meanwhile, an independent cohort obtained from the Gene Expression Omnibus (GEO) database was used for validation. The ER stress genes (ZBP1, DIABLO, GNLY, FASLG, AURKA, TNFRSF21, and CD40LG) that were associated with CM prognosis were incorporated into our prognostic model. The functional analyses indicated that the prognostic model was correlated with patient survival, gender, and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. The pathway enrichment analysis showed that the risk model was enriched in different immunity and cancer progression-associated pathways. Moreover, the signature model was significantly connected with the immune subtypes, infiltration of immune cells, immune microenvironment, as well as tumor stem cells. The gene function analysis revealed that 7 ER stress genes were differentially expressed in CM patients and were significantly associated with prognosis and several antitumor drugs. Overall, our current model presented predictive value for the prognosis of CM patients and can be further used in the development of novel therapeutic strategies for CM.

Large-scale bulk RNA-seq analysis defines immune evasion mechanism related to mast cell in gliomas

Accumulating evidence has demonstrated that the immune cells have an emerging role in controlling anti-tumor immune responses and tumor progression. The comprehensive role of mast cell in glioma has not been illustrated yet. In this study, 1,991 diffuse glioma samples were collected from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). xCell algorithm was employed to define the mast cell-related genes. Based on mast cell-related genes, gliomas were divided into two clusters with distinct clinical and immunological characteristics. The survival probability of cluster 1 was significantly lower than that of cluster 2 in the TCGA dataset, three CGGA datasets, and the Xiangya cohort. Meanwhile, the hypoxic and metabolic pathways were active in cluster 1, which were beneficial to the proliferation of tumor cells. A potent prognostic model based on mast cell was constructed. Via machine learning, DRG2 was screened out as a characteristic gene, which was demonstrated to predict treatment response and predict survival outcome in the Xiangya cohort. In conclusion, mast cells could be used as a potential effective prognostic factor for gliomas.

Glioma stem cell signature predicts the prognosis and the response to tumor treating fields treatment

INTRODUCTION

Glioma stem cells (GSCs) play an important role in glioma recurrence and chemo-radiotherapy (CRT) resistance. Currently, there is a lack of efficient treatment approaches targeting GSCs. This study aimed to explore the potential personalized treatment of patients with GSC-enriched gliomas.

METHODS

Single-cell RNA sequencing (scRNA-seq) was used to identify the GSC-related genes. Then, machine learning methods were applied for clustering and validation. The least absolute shrinkage and selection operator (LASSO) and COX regression were used to construct the risk scores. Survival analysis was performed. Additionally, the incidence of chemo-radiotherapy resistance, immunotherapy status, and tumor treating field (TTF) therapy response were evaluated in high- and low-risk scores groups.

RESULTS

Two GSC clusters exhibited significantly different stemness indices, immune microenvironments, and genomic alterations. Based on GSC clusters, 11-gene GSC risk scores were constructed, which exhibited a high predictive value for prognosis. In terms of therapy, patients with high GSC risk scores had a higher risk of resistance to chemotherapy. TTF therapy can comprehensively inhibit the malignant biological characteristics of the high GSC-risk-score gliomas.

CONCLUSION

Our study constructed a GSC signature consisting of 11 GSC-specific genes and identified its prognostic value in gliomas. TTF is a promising therapeutic approach for patients with GSC-enriched glioma.

CLSPN is a potential biomarker associated with poor prognosis in low-grade gliomas based on a multi-database analysis

BACKGROUND

The oncogene CLSPN, also known as claspin, has regulatory effects in a variety of tumours; however, it is not clear whether CLSPN is a therapeutic target in low-grade gliomas (LGG). In this study, the prognostic value of CLSPN in LGG and its role as an immunotherapeutic target were evaluated.

METHODS

Transcriptome and methylation data for thousands of patients with glioma were collected from various databases, including The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gene Expression Omnibus. Subsequently, a series of bioinformatics methods were used to evaluate the relationships between CLSPN and prognosis, clinical features, methylation status, immune cells, and molecular signaling pathways in LGG.

RESULTS

CLSPN expression levels were positively correlated with major malignant characteristics of LGG, and low expression of CLSPN was associated with a better prognosis. The methylation sites cg04263115 and cg06100291 negatively regulated the expression of CLSPN, and increased methylation levels at these sites were related to a longer survival time in patients with LGG. CLSPN was positively correlated with tumour-infiltrating immune cells and showed high copy number variation in these cells. There was a positive regulatory relationship between CLSPN expression and programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1). A gene set enrichment analysis revealed that CLSPN activates a variety of cancer signaling pathways.

CONCLUSION

CLSPN was identified as an independent risk factor for LGG with excellent prognostic value.

A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma

Background: Glioma is one of the major health problems worldwide. Biomarkers for predicting the prognosis of Glioma are still needed. Methods: The transcriptome data and clinic information on Glioma were obtained from the CGGA, TCGA, GDC, and GEO databases. The immune infiltration status in the clusters was compared. The genes with differential expression were identified, and a prognostic model was developed. Several assays were used to detect RPH3A’s role in Glioma cells, including CCK-8, colony formation, wound healing, and transwell migration assay. Results: Lower Grade Glioma (LGG) was divided into two clusters. The immune infiltration difference was observed between the two clusters. We screened for genes that differed between the two groups. WGCNA was used to construct a co-expressed network using the DEGs, and four co-expressed modules were identified, which are blue, green, grey, and yellow modules. High-risk patients have a lower overall survival rate than low-risk patients. In addition, the risk score is associated with histological subtypes. Finally, the role of RPH3A was detected. The overexpression of RPH3A in LGG cells can significantly inhibit cell proliferation and migration and regulate EMT-regulated proteins. Conclusion: Our study developed a metabolic-related model for the prognosis of Glioma cells. RPH3A is a potential therapeutic target for Glioma.

Machine learning-based identification of tumor-infiltrating immune cell-associated lncRNAs for improving outcomes and immunotherapy responses in patients with low-grade glioma

Rationale: Accumulating evidence demonstrated that long noncoding RNAs (lncRNAs) involved in the regulation of the immune system and displayed a cell-type-specific pattern in immune cell subsets. Given the vital role of tumor-infiltrating lymphocytes in effective immunotherapy, we explored the tumor-infiltrating immune cell-associated lncRNA (TIIClncRNA) in low-grade glioma (LGG), which has never been uncovered yet.

Methods: This study utilized a novel computational framework and 10 machine learning algorithms (101 combinations) to screen out TIIClncRNAs by integratively analyzing the sequencing data of purified immune cells, LGG cell lines, and bulk LGG tissues.

Results: The established TIIClnc signature based on the 16 most potent TIIClncRNAs could predict outcomes in public datasets and the Xiangya in-house dataset with decent efficiency and showed better performance when compared with 95 published signatures. The TIIClnc signature was strongly correlated to immune characteristics, including microsatellite instability, tumor mutation burden, and interferon γ, and exhibited a more active immunologic process. Furthermore, the TIIClnc signature predicted superior immunotherapy response in multiple datasets across cancer types. Notably, the positive correlation between the TIIClnc signature and CD8, PD-1, and PD-L1 was verified in the Xiangya in-house dataset.

Conclusions: The TIIClnc signature enabled a more precise selection of the LGG population who were potential beneficiaries of immunotherapy.

A signature of immune-related gene pairs (IRGPs) for risk stratification and prognosis of oral cancer patients

Background

With low response to present immunotherapy, it is imperative to identify new immune-related biomarkers for more effective immunotherapies for oral cancer.

Methods

RNA profiles for 390 oral cancer patients and 32 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and differentially expressed genes (DEGs) were analyzed. Immune genesets from ImmPort repository were overlapped with DEGs. After implementing univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, key immune-related gene pairs (IRGPs) among the overlapped DEGs for predicting the survival risk were obtained. Then, the cutoff of risk score was calculated by the receiver operating characteristic (ROC) curve to stratify oral cancer patients into high and low-risk groups. Multivariate Cox analysis was used to analyze independent prognostic indicators for oral cancer. Besides, infiltration of immune cells, functional annotation, and mutation analysis of IRGPs were conducted. Biological functions correlated with IRGPs were enriched by Gene Set Enrichment Analysis (GSEA) method.

Results

We identified 698 differentially expressed genes (DEGs) in response to oral cancer. 17 IRGPs among the DEGs were identified and integrated into a risk score model. Patients in the high-risk group have a significantly worse prognosis than those in the low-risk group in both training (P<0.001) and test (P=0.019) cohorts. Meanwhile, the IRGP model was identified as an independent prognostic factor for oral cancer. Different infiltration patterns of immune cells were found between the high- and low-risk groups that more types of T and B cells were enriched in the low-risk group. More immune-related signaling pathways were highly enriched in the low-risk group and Tenascin C (TNC) was the most frequently mutated gene. We have developed a novel 17-IRGPs signature for risk stratification and prognostic prediction of oral cancer.

Conclusion

Our study provides a foundation for improved immunotherapy and prognosis and is beneficial to the individualized management of oral cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02630-1.

Identification of m7G-associated lncRNA prognostic signature for predicting the immune status in cutaneous melanoma

RNA modifications, including RNA methylation, are widely existed in cutaneous melanoma (CM). Among epigenetic modifications, N7-methylguanosine (m7G) is a kind of modification at 5' cap of RNA which participate in maintaining the stability of mRNA and various cell biological processes. However, there is still no study concerning the relationship between CM and m7G methylation complexes, METTL1 and WDR4. Here, long non-coding RNA (lncRNAs) and gene expression data of CM from the Cancer Genome Atlas (TCGA) database were retrieved to identify differentially expressed m7G-related lncRNAs connected with overall survival of CM. Then, Cox regression analyses was applied to construct a lncRNA risk signature, the prognostic value of identified signature was further evaluated. As a result, 6 m7G-associated lncRNAs that were significantly related to CM prognosis were incorporated into our prognostic signature. The functional analyses indicated that the prognostic model was correlated with patient survival, cancer metastasis, and growth. Meanwhile, its diagnostic accuracy was better than conventional clinicopathological characteristics. The pathway enrichment analysis showed that the risk model was enriched in several immunity-associated pathways. Moreover, the signature model was significantly connected with the immune subtypes, infiltration of immune cells, immune microenvironment, as well as several m6A-related genes and tumor stem cells. Finally, a nomogram based on the calculated risk score was established. Overall, a risk signature based on 6 m7G-associated lncRNAs was generated which presented predictive value for the prognosis of CM patients and can be further used in the development of novel therapeutic strategies for CM.

PD-L2 Serves as a Potential Prognostic Biomarker That Correlates With Immune Infiltration and May Predict Therapeutic Sensitivity in Lower-Grade Gliomas

Although patients with lower-grade gliomas (LGGs; grades II and III) have a relatively favorable prognosis, patients frequently relapse and tend to progress to higher-grade gliomas, leading to treatment resistance, poor survival, and ultimately treatment failure. However, until now, thorough research has not yet been reported on the relationship between PD-L2 and immune infiltration and therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy of LGGs. In this study, we found that the expression of PD-L2 is upregulated in glioma, with high PD-L2 expression predicting a worse prognosis. Univariate and multivariate Cox regression analysis both indicated that PD-L2 represented an independent prognostic factor with high accuracy in survival prediction for LGGs. A nomogram comprising of age, grade, IDH mutation, and PD-L2 was established for predicting OS. Additionally, PD-L2 was found to be remarkably correlated with immune infiltration and some anti-tumor immune functions. The degree of PD-L2 expression was also found to be strongly related to the prediction of therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy. Furthermore, immunohistochemistry demonstrated that PD-L2 and the macrophage biomarker CD68 were both increased in glioma, with PD-L2 expression having a strong positive connection with CD68 expression. Taken together, PD-L2 is a prognostic biomarker for LGGs patients that may provide novel insights into glioma individualized therapeutic strategies and guide effective immunotherapy and chemotherapy.

Comprehensive analyses indicated the association between m6A related long non-coding RNAs and various pathways in glioma

BACKGROUND

Glioma is one of the most malignant brain tumors and diseases. N6-methyladenosine modification (m6A) is the most abundant and prevalent internal chemical modification of mRNA and long non-coding RNAs (lncRNAs) in eukaryotes. Nevertheless, the correlated pathways and clinical utilization of m6A-related lncRNAs have not been fully evaluated in glioma.

METHODS

Public RNA-sequencing and clinical annotation data were retrieved from TCGA, CGGA and GEO database. Differential expression analysis and univariate Cox regression analysis were performed to identify the m6A-related and differentially expressed lncRNAs with prognostic function (m6A-DELPF). The consensus clustering was performed to identify the expression pattern of m6A-DELPF. LASSO Cox regression analysis was performed to construct the lncRNA-based signature. The CIBERSORT and ESTIMATE algorithms were performed to analyze immune infiltration and tumor microenvironment, respectively. Immunotherapy sensitivity analysis was performed using data from TCIA. The small molecule drugs prediction analysis was performed using The Connectivity Map (CMap) database and STITCH database. A competing endogenous RNAs (ceRNA) network was constructed based on miRcode, miRDB, miRTarBase, TargetScan database.

RESULTS

Two clusters (cluster1 and cluster2) were identified after unsupervised cluster analysis based on m6A-DELPF. Additionally, a 15-gene prognostic signature namely m6A-DELPFS was constructed. Analyses of epithelial-mesenchymal-transition score, tumor microenvironment, immune infiltration, clinical characterization analysis, and putative drug prediction were performed to confirm the clinical utility and efficacy of m6A-DELPFS. The potential mechanisms including tumor immune microenvironment of m6A-DELPF influence the initiation and progression of glioma. A clinically accessible nomogram was also constructed based on the m6A-DELPF and other survival-relevant clinical parameters. Two miRNAs and 114 mRNAs were identified as the downstream of seven m6A-related lncRNAs in a ceRNA network.

CONCLUSION

Our present research confirmed the clinical value of m6A related lncRNAs and their high correlation with tumor immunity, tumor microenvironment, tumor mutation burden and drug sensitivity in glioma.