Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

Elevated HSPB1 Expression Is Associated with a Poor Prognosis in Glioblastoma Multiforme Patients

BACKGROUND

 Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer. This study investigated the clinical predictive value of heat shock protein β1 (HSPB1) in patients with GBM.

METHODS

 A correlation was established between HSPB1 expression and GBM progression using data from The Cancer Genome Atlas (TCGA) dataset, Chinese Glioma Genome Atlas dataset, Gene Expression Omnibus dataset, and Human Protein Atlas database. A survival analysis was conducted and an HSPB1-based nomogram was constructed to evaluate the prognostic value of HSPB1 in patients with GBM.

RESULTS

 Based on TCGA data mining, we discovered that HSPB1 was significantly elevated in patients with GBM and may reflect their response to immunotherapy. In survival analysis, it appeared to have a predictive role in the prognosis of patients with GBM. Five signaling pathways were significantly enriched in the high HSPB1 expression phenotype according to the gene set enrichment analysis. In addition, a significant association was found between HSPB1 expression and immune checkpoints, tumor immune infiltration, tumor immune microenvironment, and immune cell markers in glioma. Overall, our results suggest that HSPB1 may regulate the function of immune cells, serve as a new immunotherapy target, and predict the response to immunotherapy in patients with GBM.

CONCLUSION

 HSPB1 appears to serve as a potential predictor of the clinical prognosis and response to immunotherapy in patients with GBM. It may be possible to identify patients who are likely to benefit from immunotherapy by assessing the expression level of HSPB1.

Association of TNFRSF19 with a TNF family-based prognostic model and subtypes in gliomas using machine learning

Purpose

TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas.

Methods

Three databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19.

Results

Our development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified.

Conclusions

The TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas.

Predictive Panel for Immunotherapy in Low-Grade Glioma

BACKGROUND

The main treatment of low-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens.

METHODS

We integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis, and TIDE analysis were used to identify the immune characteristics of 3 immune subtypes and the feasibility of 5 antigens as immune checkpoint markers.

RESULTS

We analyzed the isolation and mutation of homologous recombination repair genes (HRR) of the 3 immune subtypes, and the HRR genes of the 3 subtypes were obviously segregated. Among them, the IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors-this is consistent with the results of tumor mutation load analysis of 3 immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in the IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the 3 subtypes, we found that they were upregulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG; through TIDE analysis and TISIDB analysis, we obtained 5 markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis.

CONCLUSIONS

We obtained 3 reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.

TANK shapes an immunosuppressive microenvironment and predicts prognosis and therapeutic response in glioma

Background

Glioma, the most prevalent malignant intracranial tumor, poses a significant threat to patients due to its high morbidity and mortality rates, but its prognostic indicators remain inaccurate. Although TRAF-associated NF-kB activator (TANK) interacts and cross-regulates with cytokines and microenvironmental immune cells, it is unclear whether TANK plays a role in the immunologically heterogeneous gliomas.

Methods

TANK mRNA expression patterns in public databases were analyzed, and qPCR and IHC were performed in an in-house cohort to confirm the clinical significance of TANK. Then, we systematically evaluated the relationship between TANK expression and immune characteristics in the glioma microenvironment. Additionally, we evaluated the ability of TANK to predict treatment response in glioma. TANK-associated risk scores were developed by LASSO-Cox regression and machine learning, and their prognostic ability was tested.

Results

TANK was specifically overexpressed in glioma and enriched in the malignant phenotype, and its overexpression was related to poor prognosis. The presence of a tumor microenvironment that is immunosuppressive was evident by the negative correlations between TANK expression and immunomodulators, steps in the cancer immunity cycle, and immune checkpoints. Notably, treatment for cancer may be more effective when immunotherapy is combined with anti-TANK therapy. Prognosis could be accurately predicted by the TANK-related risk score.

Conclusions

High expression of TANK is associated with the malignant phenotype of glioma, as it shapes an immunosuppressive tumor microenvironment. Additionally, TANK can be used as a predictive biomarker for responses to various treatments and prognosis.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Integrative analysis of a novel super-enhancer-associated lncRNA prognostic signature and identifying LINC00945 in aggravating glioma progression

BACKGROUND

Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target.

METHODS

Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model.

RESULTS

6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo.

CONCLUSION

Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

Co-amplified with PDGFRA, IGFBP7 is a prognostic biomarker correlated with the immune infiltrations of glioma

BACKGROUND

A subgroup of glioma carry genetic 4q12 amplification including platelet derived growth factor receptor α (PDGFRA) and insulin like growth factor binding protein 7 (IGFBP7). However, the prognosis of PDGFRA and IGFBP7 in glioma is unclear.

METHODS

The prognosis of PDGFRA and IGFBP7 was determined using cox regression and Kaplan-Meier survival analysis. Pathways associated with IGFBP7 were analyzed through gene set enrichment analysis (GSEA). Immune profiling of glioma was determined using "ESTIMATE" and "TIMER" database.

RESULTS

PDGFRA amplification or expression was not correlated with the outcomes of glioblastoma (GBM). IGFBP7 but not PDGFRA was over-expressed in GBM. IGFBP7 over-expression was correlated with the unfavorable outcomes of GBM. In lower grade glioma (LGG), PDGFRA over-expression was not correlated with the unfavorable prognosis of LGG, while, IGFBP7 was a prognostic biomarker of LGG. LGG patients with IGFBP7 lower expressions had prolonged clinical overall survival. Combination of IDH mutation, LGG grade and IGFBP7 achieved even better prognostic effects in LGG. Moreover, IGFBP7 was over-expressed in glioma patients with wild type IDH or with high grades. IGFBP7 over-expression was correlated with the unfavorable outcomes of glioma. Furthermore, IGFBP7 was hypo-methylated in GBM or LGG patients without IDH mutations. IGFBP7 hyper-methylation was correlated with the lower overall survival of GBM or LGG. LGG patients with wild type IDH and with IGFBP7 hypo-methylation demonstrated even worse prognosis. IGFBP7 was associated with multiple immune-related signaling pathways in GBM or LGG. The stromal score, immune score and the infiltrations of immune cells were also correlated with IGFBP7 and the prognosis of LGG.

CONCLUSIONS

IGFBP7 but not PDGFRA served an ideal prognostic marker and therapeutic target of glioma.

MARCKS is a New Prognostic Biomarker in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common type of cancers, but there is still a lack of known biomarkers for the effective diagnosis or prognosis of HCC. Myristoylated alanine-rich C-kinase substrate (MARCKS) is a substrate of protein kinase C, which was located in the cell plasma membrane. The purpose of our study was to evaluate the role of MARCKS in HCC.

Methods

The role of MARCKS in HCC was explored by bioinformatics and experiment.

Results

We demonstrated that MARCKS expression was significantly elevated in HCC datasets of TCGA. MARCKS was up-regulated in tumor sample in HCC. Functional enrichment indicated that MARCKS-related differentially expressed genes (DEGs) were mainly enriched in cell junction tissue, response to growth factors and cell population proliferation. Tumor and ECM-receptor interactions related pathways were enriched by the KEGG. MARCKS expression in HCC patients was higher in females, younger individuals, and those at worse clinical stages. Cox regression analysis showed that MARCKS expression was a risk factor for overall survival and disease-specific survival of patients.

Conclusion

MARCKS was up-regulated in HCC, may play a crucial role in HCCs, and has prognostic value for clinical outcomes.

Comprehensive Pan-Cancer Analysis of GINS2 for Human Tumour Prognosis and as an Immunological Biomarker

BACKGROUND

In recent years, more and more reports have shown that GINS complex subunit 2 (GINS2) plays an important role in the occurrence and progression of tumours. However, there is a lack of comprehensive and systematic research on its prognostic and immune effects in pan-cancer. Therefore, this study is aimed at investigating the prognostic value and immune-related role of GINS2 in human tumours and providing a comprehensive understanding of its carcinogenic mechanism in pan-cancer.

METHODS

We investigated different databases, including TIMER, TCGA, GTEX, CPTAC, GEPIA, and SangerBox. The study was carried out on the expression and prognosis of GINS2 in human tumours, immune infiltration and microenvironment, immune checkpoints, neoantigens, tumour mutational burden, microsatellite instability, mismatch repair (MMR) genes, methylation, cancer-associated fibroblasts (CAFs), and enrichment analysis of gene set.

RESULTS

GINS2 plays a potential carcinogenic role in various human tumours through mRNA and protein levels. It is highly expressed in most cancers, and its expression is significantly correlated with tumour prognosis. In addition, the expression of GINS2 is associated with immune microenvironment and immune infiltration, especially in brain lower-grade glioma, lung squamous cell carcinoma, TGCT, breast invasive carcinoma, and glioblastoma multiforme. At the same time, GINS2 is related to immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. It also affects the expression of DNA MMR genes and methyltransferase in pan-cancer. Finally, the correlation between GINS2 and CAF abundance in most tumours was studied, and an enrichment analysis of GINS2 and its related proteins was also carried out.

CONCLUSION

This is the first study on GINS2 as a prognostic and immune mechanism in pan-cancer. GINS2 may be a valuable prognostic immunological biomarker of pan-cancer. This paper provides a relatively comprehensive understanding on the correlation of GINS2 with pan-cancer.

CAPN8 involves with exhausted, inflamed, and desert immune microenvironment to influence the metastasis of thyroid cancer

BACKGROUND

Thyroid cancer (THCA) is the most prevalent malignant disease of the endocrine system, in which 5-year survival can attain about 95%, but patients with metastasis have a poor prognosis. Very little is known about the role of CAPN8 in the metastasis of THCA. In particular, the effect of CAPN8 on the tumor immune microenvironment (TIME) and immunotherapy response is unclear.

MATERIAL AND METHODS

Multiome datasets and multiple cohorts were acquired for analysis. Firstly, the expression and the prognostic value of CAPN8 were explored in public datasets and in vitro tumor tissues. Then, hierarchical clustering analysis was performed to identify the immune subtypes of THCA according to the expression of CAPN8 and the activities of related pathways. Subsequent analyses explored the different patterns of TIME, genetic alteration, DNA replication stress, drug sensitivity, and immunotherapy response among the three immune phenotypes. Finally, five individual cohorts of thyroid cancer were utilized to test the robustness and extrapolation of the three immune clusters.

RESULTS

CAPN8 was found to be a significant risk factor for THCA with a markedly elevated level of mRNA and protein in tumor tissues. This potential oncogene could induce the activation of epithelial-mesenchymal transition and E2F-targeted pathways. Three subtypes were identified for THCA, including immune exhausted, inflamed, and immune desert phenotypes. The exhausted type was characterized by a markedly increased expression of inhibitory receptors and infiltration of immune cells but was much more likely to respond to immunotherapy. The immune desert type was resistant to common chemotherapeutics with extensive genomic mutation and copy number variance.

CONCLUSION

The present study firstly explored the role of CAPN8 in the metastasis of THCA from the aspects of TIME. Three immune subtypes were identified with quite different patterns of prognosis, immunotherapy response, and drug sensitivity, providing novel insights for the treatment of THCA and helping understand the cross-talk between CAPN8 and tumor immune microenvironment.

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

Integrated analysis of inflammatory response subtype-related signature to predict clinical outcomes, immune status and drug targets in lower-grade glioma

Background: The inflammatory response in the tumor immune microenvironment has implications for the progression and prognosis in glioma. However, few inflammatory response-related biomarkers for lower-grade glioma (LGG) prognosis and immune infiltration have been identified. We aimed to construct and identify the prognostic value of an inflammatory response-related signature, immune infiltration, and drug targets for LGG.

Methods: The transcriptomic and clinical data of LGG samples and 200 inflammatory response genes were obtained from public databases. The LGG samples were separated into two inflammatory response-related subtypes based on differentially expressed inflammatory response genes between LGG and normal brain tissue. Next, inflammatory response-related genes (IRRGs) were determined through a difference analysis between the aforementioned two subtypes. An inflammatory response-related prognostic model was constructed using IRRGs by using univariate Cox regression and Lasso regression analyses and validated in an external database (CGGA database). ssGSEA and ESTIMATE algorithms were conducted to evaluate immune infiltration. Additionally, we performed integrated analyses to investigate the correlation between the prognostic signature and N 6-methyladenosine mRNA status, stemness index, and drug sensitivity. We finally selected MSR1 from the prognostic signature for further experimental validation.

Results: A total of nine IRRGs were identified to construct the prognostic signature for LGG. LGG patients in the high-risk group presented significantly reduced overall survival than those in the low-risk group. An ROC analysis confirmed the predictive power of the prognostic model. Multivariate analyses identified the risk score as an independent predictor for the overall survival. ssGSEA revealed that the immune status was definitely disparate between two risk subgroups, and immune checkpoints such as PD-1, PD-L1, and CTLA4 were significantly expressed higher in the high-risk group. The risk score was strongly correlated with tumor stemness and m6A. The expression levels of the genes in the signature were significantly associated with the sensitivity of tumor cells to anti-tumor drugs. Finally, the knockdown of MSR1 suppressed LGG cell migration, invasion, epithelial–mesenchymal transition, and proliferation.

Conclusion: The study constructed a novel signature composed of nine IRRGs to predict the prognosis, potential drug targets, and impact immune infiltration status in LGG, which hold promise for screening prognostic biomarkers and guiding immunotherapy for LGG.

TRP Family Genes Are Differently Expressed and Correlated with Immune Response in Glioma

(1) Background: glioma is the most prevalent primary tumor of the human central nervous system and accompanies extremely poor prognosis in patients. The transient receptor potential (TRP) channels family consists of six different families, which are closely associated with cancer cell proliferation, differentiation, migration, and invasion. TRP family genes play an essential role in the development of tumors. Nevertheless, the function of these genes in gliomas is not fully understood. (2) Methods: we analyze the gene expression data of 28 TRP family genes in glioma patients through bioinformatic analysis. (3) Results: the study showed the aberrations of TRP family genes were correlated to prognosis in glioma. Then, we set enrichment analysis and selected 10 hub genes that may play an important role in glioma. Meanwhile, the expression of 10 hub genes was further established according to different grades, survival time, IDH mutation status, and 1p/19q codeletion status. We found that TRPC1, TRPC3, TRPC4, TRPC5, TRPC6, MCOLN1, MCOLN2, and MCOLN3 were significantly correlated to the prognosis in glioma patients. Furthermore, we illustrated that the expression of hub genes was associated with immune activation and immunoregulators (immunoinhibitors, immunostimulators, and MHC molecules) in glioma. (4) Conclusions: we proved that TRP family genes are promising immunotherapeutic targets and potential clinical biomarkers in patients with glioma.

Identification of Potential Diagnoses Based on Immune Infiltration and Autophagy Characteristics in Major Depressive Disorder

Background: Major depressive disorder (MDD) is a serious mental illness characterized by mood changes and high suicide rates. However, no studies are available to support a blood test method for MDD diagnosis. The objective of this research was to identify potential peripheral blood biomarkers for MDD and characterize the novel pathophysiology.

Methods: We accessed whole blood microarray sequencing data for MDD and control samples from public databases. Biological functions were analysed by GO and KEGG pathway enrichment analyses using the clusterprofile R package. Infiltrated immune cell (IIC) proportions were identified using the CIBERSORT algorithm. Clustering was performed using the ConsensusClusterPlus R package. Protein–protein interactions (PPI) were assessed by constructing a PPI network using STRING and visualized using Cytoscape software. Rats were exposed to chronic unpredictable mild stress (CUMS) for 6 weeks to induce stress behaviour. Stress behaviour was evaluated by open field experiments and forced swimming tests. Flow cytometry was used to analyse the proportion of CD8⁺ T cells. The expression of the corresponding key genes was detected by qRT–PCR.

Results: We divided MDD patients into CD8H and CD8L clusters. The functional enrichment of marker genes in the CD8H cluster indicated that autophagy-related terms and pathways were significantly enriched. Furthermore, we obtained 110 autophagy-related marker genes (ARMGs) in the CD8H cluster through intersection analysis. GO and KEGG analyses further showed that these ARMGs may regulate a variety of autophagy processes and be involved in the onset and advancement of MDD. Finally, 10 key ARMGs were identified through PPI analysis: RAB1A, GNAI3, VAMP7, RAB33B, MYC, LAMP2, RAB11A, HIF1A, KIF5B, and PTEN. In the CUMS model, flow cytometric analysis confirmed the above findings. qRT–PCR revealed significant decreases in the mRNA levels of Gnai3, Rab33b, Lamp2, and Kif5b in the CUMS groups.

Conclusion: In this study, MDD was divided into two subtypes. We combined immune infiltrating CD8⁺ T cells with autophagy-related genes and screened a total of 10 ARMG genes. In particular, RAB1A, GNAI3, RAB33B, LAMP2, and KIF5B were first reported in MDD. These genes may offer new hope for the clinical diagnosis of MDD.

A Novel Defined Pyroptosis-Related Gene Signature for Predicting Prognosis and Treatment of Glioma

Pyroptosis, a form of programmed cell death, that plays a significant role in the occurrence and progression of tumors, has been frequently investigated recently. However, the prognostic significance and therapeutic value of pyroptosis in glioma remain undetermined. In this research, we revealed the relationship of pyroptosis-related genes to glioma by analyzing whole transcriptome data from The Cancer Genome Atlas (TCGA) dataset serving as the training set and the Chinese Glioma Genome Atlas (CGGA) dataset serving as the validation set. We identified two subgroups of glioma patients with disparate prognostic and clinical features by performing consensus clustering analysis on nineteen pyroptosis-related genes that were differentially expressed between glioma and normal brain tissues. We further derived a risk signature, using eleven pyroptosis-related genes, that was demonstrated to be an independent prognostic factor for glioma. Furthermore, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to implement functional analysis of our gene set, and the results were closely related to immune and inflammatory responses in accordance with the characteristics of pyroptosis. Moreover, Gene Set Enrichment Analysis (GSEA) results showed that that the high-risk group exhibited enriched characteristics of malignant tumors in accordance with its poor prognosis. Next, we analyzed different immune cell infiltration between the two risk groups using ssGSEA. Finally, CASP1 was identified as a core gene, so we subsequently selected an inhibitor targeting CASP1 and simulated molecular docking. In addition, the inhibitory effect of belnacasan on glioma was verified at the cellular level. In conclusion, pyroptosis-related genes are of great significance for performing prognostic stratification and developing treatment strategies for glioma.

Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

Backgrounds

Epithelial-mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).

Methods

EMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.

Results

A total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N ⁶-methyladenosine (m⁶A) RNA expression, microsatellite instability level, and stemness indices.

Conclusion

We constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.

The clinical features, prognostic significance, and immune heterogeneity of CD37 in diffuse gliomas

Diffuse glioma is the most prevalent and malignant brain tumor. The function and significance of CD37 in diffuse gliomas remain largely unknown. Here, we showed CD37 was abnormally expressed in diverse cancers, especially glioma by pan-cancer differential expression analysis. In addition, we found CD37 was upregulated in higher grade and IDH or IDH1-wildtype gliomas, which was further validated by qPCR and IHC. Survival analysis revealed CD37 served as an independent indicator for unfavorable prognosis of patients with diffuse gliomas. Functional enrichment analysis revealed CD37 was associated with immunological processes. Moreover, immune infiltration analyses suggested gliomas with high-expression CD37 had greater infiltration of M2 macrophages and neutrophils, and lower NK cell abundance. CD37 was closely correlated to immune checkpoint molecules, including CD276, CD80, CD86, and PD-L2. Our results indicated CD37 is an independent prognostic factor and plays an immunosuppressive role in diffuse gliomas. Targeting CD37 could be a promising immunotherapeutic strategy for diffuse gliomas.

Immune classification and identification of prognostic genes for uveal melanoma based on six immune cell signatures

Cutaneous melanoma could be treated by immunotherapy, which only has limited efficacy on uveal melanoma (UM). UM immunotyping for predicting immunotherapeutic responses and guiding immunotherapy should be better understood. This study identified molecular subtypes and key genetic markers associated with immunotherapy through immunosignature analysis. We screened a 6-immune cell signature simultaneously correlated with UM prognosis. Three immune subtypes (IS) were determined based on the 6-immune cell signature. Overall survival (OS) of IS3 was the longest. Significant differences of linear discriminant analysis (LDA) score were detected among the three IS types. IS3 with the highest LDA score showed a low immunosuppression. IS1 with the lowest LDA score was more immunosuppressive. LDA score was significantly negatively correlated with most immune checkpoint-related genes, and could reflect UM patients’ response to anti-PD1 immunotherapy. Weighted correlation network analysis (WGCNA) identified that salmon, purple, yellow modules were related to IS and screened 6 prognostic genes. Patients with high-expressed NME1 and TMEM255A developed poor prognosis, while those with high-expressed BEX5 and ROPN1 had better prognosis. There was no notable difference in OS between patients with high-expressed LRRN1 and ST13 and those with low-expressed LRRN1 and ST13. NME1, TMEM255A, Bex5 and ROPN1 showed potential prognostic significance in UM.