Identification of purity and prognosis-related gene signature by network analysis and survival analysis in brain lower grade glioma

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BACKGROUND

Numerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.

METHODS

Single-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.

RESULTS

The established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.

CONCLUSION

We developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Phospholipid scramblase 1 acts through the IL-6/JAK/STAT3 pathway to promote the malignant progression of glioma

BACKGROUND

Phospholipid scramblase 1 (PLSCR1) is a calcium-dependent endofacial plasma-membrane protein that plays an essential role in multiple human cancers. However, little is known about its role in glioma. This study aimed to investigate PLSCR1 function in glioma, and elucidate its underlying molecular mechanisms.

METHODS

PLSCR1 expression in human glioma cell lines (U87MG, U251, LN229, A172 and T98G) and human astrocytes was detected by western blot and qRT-PCR. PLSCR1 was silenced using si-PLSCR1-1 and si-PLSCR1-2 in LN229 and U251 cells. PLSCR1 was overexpressed using the pcDNA-PLSCR1 plasmid in T98G cells. Colony formation, 5-ethynyl-2'-deoxyuridine, flow cytometry and transwell assays were employed for measuring cell proliferation, apoptosis and mobility after PLSCR1 knockdown or overexpression. PLSCR1 function in glycolysis in glioma cells was determined through measuring the extracellular acidification rate, oxygen consumption rate, glucose consumption and lactate production. Besides, immunohistochemistry, western blot and qRT-PCR were utilized to assess mRNA and protein expression. Besides, the effect of PLSCR1 silencing on subcutaneous tumor was also monitored.

RESULTS

PLSCR1 expression was upregulated in glioma. The downregulation of PLSCR1 repressed the proliferation, mobility, epithelial-to-mesenchymal transition (EMT) and glycolysis; however, it facilitated apoptosis in glioma cells. Whereas, PLSCR1 upregulation had the opposite effect. Moreover, PLSCR1 promoted the activation of the IL-6/JAK/STAT3 pathway in glioma cells. Besides, IL-6 treatment significantly reversed the function of PLSCR1 silencing on cell proliferation, mobility, EMT, apoptosis and glycolysis. In a nude mouse tumor model, silencing PLSCR1 suppressed tumor growth via inactivating IL-6/JAK/STAT3 signaling.

CONCLUSION

Our results indicated that PLSCR1 could facilitate proliferation, mobility, EMT and glycolysis, but repress apoptosis through activating IL-6/JAK/STAT3 signaling in glioma. Therefore, PLSCR1 may function as a potential therapeutic target for glioma.

ITGB4 upregulation is associated with progression of lower grade glioma

Gliomas originating in the neuroepithelium account for about 80% of brain malignancies and are the most common cancer of the central nervous system. Clinical management of gliomas remains challenging despite significant advances in comprehensive therapies, including radiotherapy, chemotherapy, and surgery. The ITGB4 (Integrin subunit beta 4) gene encodes a receptor for laminins and its upregulation in tumor tissues is associated with poor prognosis. However, its role in glioma is not well understood. First, we performed a pan cancer analysis of ITGB4 expression in The Cancer Genome Atlas (TCGA) dataset. Survival analysis was done on Chinese Glioma Genome Atlas (CGGA) and TCGA. Immunohistochemistry was then used to validate the expression and role of ITGB4 in glioma. We finally analyzed the possible mechanism by immune infiltration and single-cell sequencing analysis. Here, we found that ITGB4 is upregulated in glioma and accurately predicts the prognosis of lower grade glioma (LGG). Univariate and multivariate Cox regression analyses showed that ITGB4 is a risk factor for LGG. Immunohistochemical analysis confirmed that ITGB4 accurately predicts LGG prognosis. Non-negative matrix factorization (NMF) cluster analysis showed that ITGB4 was closely related to immune related genes. Immune cell infiltration and single cell sequencing analyses indicated that ITGB4 may be closely related to the microenvironment of gliomas, especially tumor-associated fibroblasts. ITGB4 is a promising diagnostic and therapeutic factor in LGG patients.

Construction and validation of a risk model based on the key SNARE proteins to predict the prognosis and immune microenvironment of gliomas

Background

Synaptic transmission between neurons and glioma cells can promote glioma progression. The soluble N-ethylmaleimide-sensitive fusion factor attachment protein receptors (SNARE) play a key role in synaptic functions. We aimed to construct and validate a novel model based on the SNARE proteins to predict the prognosis and immune microenvironment of glioma.

Methods

Differential expression analysis and COX regression analysis were used to identify key SRGs in glioma datasets, and we constructed a prognostic risk model based on the key SRGs. The prognostic value and predictive performance of the model were assessed in The Cancer Genome Atlas (TCGA) and Chinese glioma Genome Atlas (CGGA) datasets. Functional enrichment analysis and immune-related evaluation were employed to reveal the association of risk scores with tumor progression and microenvironment. A prognostic nomogram containing the risk score was established and assessed by calibration curves and time-dependent receiver operating characteristic curves. We verified the changes of the key SRGs in glioma specimens and cells by real-time quantitative PCR and Western blot analyses.

Results

Vesicle-associated membrane protein 2 (VAMP2) and vesicle-associated membrane protein 5 (VAMP5) were identified as two SRGs affecting the prognoses of glioma patients. High-risk patients characterized by higher VAMP5 and lower VAMP2 expression had a worse prognosis. Higher risk scores were associated with older age, higher tumor grades, IDH wild-type, and 1p19q non-codeletion. The SRGs risk model showed an excellent predictive performance in predicting the prognosis in TCGA and CGGA datasets. Differentially expressed genes between low- and high-risk groups were mainly enriched in the pathways related to immune infiltration, tumor metastasis, and neuronal activity. Immune score, stromal score, estimate score, tumor mutational burden, and expression of checkpoint genes were positively correlated with risk scores. The nomogram containing the risk score showed good performance in predicting the prognosis of glioma. Low VAMP2 and high VAMP5 were found in different grades of glioma specimens and cell lines.

Conclusion

We constructed and validated a novel risk model based on the expression of VAMP2 and VAMP5 by bioinformatics analysis and experimental confirmation. This model might be helpful for clinically predicting the prognosis and response to immunotherapy of glioma patients.

miR-21-5p inhibits the growth of brain glioma cells through regulating the glycolysis mediated by PFKFB2

Brain glioma is a common gynecological tumor. MicroRNA (miRNA) plays a very important role in the pathogenesis and development of tumors. It was found that glycolysis played important regulatory roles in tumor growth. The present study aims to investigate the expression pattern of miR-21-5p in brain glioma cells. We examined miR-21-5p and PFKFB2 levels in brain glioma cells via qRT-PCR. Then we performed CCK-8 and Transwell migration assays and determined glucose uptake and lactose production to unveil the properties of miR-21-5p in invasion, cell viability, along with glycolysis in brain glioma cells. Luciferase activity assay was implemented to elucidate if PFKFB2 was a miR-21-5p target gene. Western blotting and qRT-PCR were executed to further validate that miR-21-5p targeted PFKFB2. We repeated these functional assays to observe whether miR-21-5p could impede the function of PFKFB2. qRT-PCR signified that miR-21-5p was elevated in brain glioma tissues in contrast to matching adjacent normal tissues. Functional assays disclosed that elevation of miR-21-5p promoted cell viability, invasion, together with glycolysis. Luciferase assay indicated that PFKFB2 was a miR-21-5p target gene. Moreover, miR-21-inhibit could hinder cell viability, invasion, and glycolysis triggered by overexpression of PFKFB2 in brain glioma cells. miR-21-5p level is elevated in brain glioma and can impede brain glioma cell growth via regulating the glycolysis mediated by PFKFB2, thus is a potential target of treating brain glioma.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Pan-cancer analysis of the angiotensin II receptor-associated protein as a prognostic and immunological gene predicting immunotherapy responses in pan-cancer

Background: Understanding interior molecular mechanisms of tumorigenesis and cancer progression contributes to antitumor treatments. The angiotensin II receptor-associated protein (AGTRAP) has been confirmed to be related with metabolic products in metabolic diseases and can drive the progression of hepatocellular carcinoma and colon carcinoma. However, functions of AGTRAP in other kinds of cancers are unclear, and a pan-cancer analysis of AGTRAP has not been carried out.

Methods and materials: We downloaded data from The Cancer Genome Atlas and Genotype-Tissue Expression dataset and The Human Protein Atlas databases and then used R software (version 4.1.1) and several bioinformatic tools to conduct the analysis.

Results: In our study, we evaluated the expression of AGTRAP in cancers, such as high expression in breast cancer, lung adenocarcinoma, and glioma and low expression in kidney chromophobe. Furthermore, our study revealed that high expression of AGTRAP is significantly related with poor prognosis in glioma, liver cancer, kidney chromophobe, and so on. We also explored the putative functional mechanisms of AGTRAP across pan-cancer, such as endoplasmic reticulum pathway, endocytosis pathway, and JAK-STAT signaling pathway. In addition, the connection between AGTRAP and tumor microenvironment, tumor mutation burden, and immune-related genes was proven.

Conclusion: Our study provided comprehensive evidence of the roles of AGTRAP in different kinds of cancers and supported the relationship of AGTRAP and tumorous immunity.

Immune Infiltration Associated MAN2B1 Is a Novel Prognostic Biomarker for Glioma

Mannosidase Alpha Class 2B Member 1 (MAN2B1) gene encodes lysosomal alpha-d-mannosidase involved in the ordered degradation of N-linked glycoproteins. Alteration in MAN2B1 has been proved to be accountable for several diseases. However, the relationship between MAN2B1 and glioma malignancy remains unclear. In this study, RNA-seq data from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas datasets were analyzed to explore the correlation between MAN2B1 and clinicopathological features, prognosis, and somatic mutations in gliomas. We found that MAN2B1 was elevated in glioma and was correlated with malignant clinical and molecular features. Upregulated expression of MAN2B1 is prognostic for poor outcomes in glioma patients. Different frequencies of somatic mutations were found in gliomas between high and low MAN2B1 expression. Real-time quantitative polymerase chain reaction, western blot, and immunohistochemistry staining from glioma patient samples and cell lines were used to validate bioinformatic findings. Functional enrichment analysis showed that MAN2B1 was involved in immune and inflammation processes. Moreover, MAN2B1 expression was strongly correlated with M2 macrophages and weakly correlated with M1 macrophages. Further analysis confirmed that MAN2B1 was closely associated with the markers of M2 macrophages and tumor-associated macrophages. Taken together, MAN2B1 is a potential prognostic biomarker in glioma and associates with immune infiltration.

AGTRAP Is a Prognostic Biomarker Correlated With Immune Infiltration in Hepatocellular Carcinoma

Background

Recently, it has been reported that angiotensin II receptor-associated protein (AGTRAP) plays a substantial role in tumor progression. Nevertheless, the possible role of AGTRAP in hepatocellular carcinoma (HCC) remains unrecognized.

Methods

The metabolic gene rapid visualizer, Cancer Cell Line Encyclopedia, Human Protein Atlas, and Hepatocellular Carcinoma Database were used to analyze the expression of AGTRAP in HCC tissues and normal liver tissues or adjacent tissues. Kaplan-Meier plotter and UALCAN analysis were used to assess the prognostic and diagnostic value of AGTRAP. LinkedOmics and cBioPortal were used to explore the genes co-expressed with AGTRAP in HCC. To further understand the potential mechanism of AGTRAP in HCC, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment pathway analyses were performed using R software, the protein-protein interaction (PPI) network was established using the STRING database, and the immune infiltration and T-cell exhaustion related to AGTRAP were explored via Timer and GEPIA. In addition, immunohistochemistry was used to detect the expression of AGTRAP protein in HCC tissues and paired adjacent tissues from clinical specimens.

Results

This study found that the mRNA and protein levels of AGTRAP in HCC tissues were higher than those in normal liver tissues and adjacent tissues, and higher mRNA levels of AGTRAP were associated with higher histological grade and a poor overall survival in HCC patients. The area under the receiver operating characteristic curve (AUC) of AGTRAP was 0.856, suggesting that it could be a diagnostic marker for HCC. Moreover, the alteration rate of AGTRAP in HCC was 8%, and AGTRAP was involved in HCC probably through the NF-κB and MAPK signaling pathways. Furthermore, AGTRAP was positively correlated with the infiltration of CD8⁺ T cells, CD4⁺ T cells, B cells, macrophages, dendritic cells, and neutrophils, and the levels of AGTRAP were significantly correlated with T-cell exhaustion biomarkers. The immunohistochemistry results confirmed that the protein levels of AGTRAP were consistently higher in HCC tissues than in paired adjacent tissues.

Conclusion

The clinical value of AGTRAP and its correlation with immune infiltration in HCC was effectively identified in clinical data from multiple recognized databases. These findings indicate that AGTRAP could serve as a potential biomarker in the treatment of HCC, thereby informing its prognosis, diagnosis, and even immunotherapy.

Long Noncoding RNA OR7E156P/miR-143/HIF1A Axis Modulates the Malignant Behaviors of Glioma Cell and Tumor Growth in Mice

Gliomas are characterized by high incidence, recurrence and mortality all of which are significant challenges to efficacious clinical treatment. The hypoxic microenvironment in the inner core and intermediate layer of the tumor mass of gliomas is a critical contributor to glioma pathogenesis. In this study, we identified an upregulated lncRNA, OR7E156P, in glioma was identified. The silencing of OR7E156P inhibited cell invasion and DNA synthesis in vitro and tumor growth in vivo. OR7E156P was intricately linked to the HIF1A pathway. Hypoxia could induce OR7E156P expression, whereas OR7E156P silencing decreased HIF1A protein levels under hypoxic conditions. Hypoxia promoted glioma cell invasion and DNA synthesis, and HUVEC tube formation, whereas OR7E156P silencing partially reversed the cellular effects of hypoxia. HIF1A overexpression promoted, whereas OR7E156P silencing inhibited tumor growth; the inhibitory effects of OR7E156P silencing on tumor growth were partially reversed by HIF1A overexpression. miR-143 directly targeted OR7E156P and HIF1A, respectively. miR-143 inhibition increased HIF1A protein levels, promoted glioma cell invasion and DNA synthesis. Moreover, they enhanced HUVEC tube formation, whereas OR7E156P silencing partially reversed the cellular effects of miR-143 inhibition. HIF1A targeted the promoter region of miR-143 and inhibited miR-143 expression. Altogether a regulatory axis consisting of OR7E156P, miR-143, and HIF1A, was identified which is deregulated in glioma, and the process of the OR7E156P/miR-143/HIF1A axis modulating glioma cell invasion through ZEB1 and HUVEC tube formation through VEGF was demonstrated.

Establishment of a nomogram with EMP3 for predicting clinical outcomes in patients with glioma: A bi-center study

Aim

To demonstrate the clinical value of epithelial membrane protein 3 (EMP3) with bioinformatic analysis and clinical data, and then to establish a practical nomogram predictive model with bicenter validation.

Methods

The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis. Then, we analyzed EMP3 expression in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts.

Results

EMP3 was significantly higher in higher‐grade glioma and predicted poor prognosis. In multivariate analysis, high expression of EMP3 (HR = 2.842, 95% CI 1.984–4.071), WHO grade (HR = 1.991, 95% CI 1.235–3.212), and IDH1 mutant (HR = 0.503, 95% CI 0.344–0.737) were included. The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes.

Conclusion

This study demonstrated EMP3 as a novel predictor for clinical progression and clinical outcomes in glioma. Moreover, the nomogram with EMP3 expression represented a practical approach to provide individualized risk assessment for glioma patients.

Subtype-dependent regulation of Gβγ signalling

G protein-coupled receptors (GPCRs) transmit information to the cell interior by transducing external signals to heterotrimeric G protein subunits, Gα and Gβγ subunits, localized on the inner leaflet of the plasma membrane. Though the initial focus was mainly on Gα-mediated events, Gβγ subunits were later identified as major contributors to GPCR-G protein signalling. A broad functional array of Gβγ signalling has recently been attributed to Gβ and Gγ subtype diversity, comprising 5 Gβ and 12 Gγ subtypes, respectively. In addition to displaying selectivity towards each other to form the Gβγ dimer, numerous studies have identified preferences of distinct Gβγ combinations for specific GPCRs, Gα subtypes and effector molecules. Importantly, Gβ and Gγ subtype-dependent regulation of downstream effectors, representing a diverse range of signalling pathways and physiological functions have been found. Here, we review the literature on the repercussions of Gβ and Gγ subtype diversity on direct and indirect regulation of GPCR/G protein signalling events and their physiological outcomes. Our discussion additionally provides perspective in understanding the intricacies underlying molecular regulation of subtype-specific roles of Gβγ signalling and associated diseases.

Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune‐related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune‐related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune‐related biomarkers for metastatic melanoma.

Prediction of ATPase cation transporting 13A2 molecule in Petromyzon marinus and pan-cancer analysis into human tumors from an evolutionary perspective

The ATPase cation transporting 13A2 protein (ATP13A2), which maintains the homeostasis of mitochondria and lysosomes, plays a significant role in human neurodegenerative diseases and cancer. Through constructing a lamprey proteome database, employing multiple sequence alignment and phylogenetic analysis, 5 ATP13A2 proteins from Petromyzon marinus (Pm-ATP13A2) were identified based on the evolutionary perspective. The motif and domain analysis showed that the ATP13A2 protein was conserved. The multiple phosphorylation sites and transmembrane structures highlighted the characteristics of ATP13A2 as the P-ATPase-V cation transporting protein. Based on the information provided by the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, this study was conducted as a preliminary investigation of the carcinogenic effects of the ATP13A2 gene in a variety of tumors. The ATP13A2 was strongly expressed in most tumors, except in two types of nervous system tumors glioblastoma multiforme (GBM) and brain lower grade glioma (LGG). Moreover, the expression of ATP13A2 was strongly correlated with the prognosis of tumor patients. The high expression of ATP13A2 was obviously related to the poor prognosis of LGG. The poor prognosis of LGG patients may affect the ATP13A2 expression through the immune cells and radiotherapy. Also, cancer-related fibroblast infiltration was observed. All in all, this work offers more insights into the molecular evolution of the ATP13A2 protein and facilitates the understanding of the carcinogenic effects of the ATP13A2 in different tumors. Our discussion also promotes the study into the successful evolution of the vertebrate brain and the mechanism of clinical brain-related diseases.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects.

Methods

TCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis, and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the four genes and tumor stages was constructed and confirmed in two GEO data sets.

Results

Among the three subtypes—high, medium, and low immune infiltration subtype—the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The four key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2, and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these four genes through the S1PR1. The risk score signature based on these four genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets.

Conclusions

The prognosis of LUAD patients could be predicted by the constructed risk score signature based on the four genes, making this score a potential independent biomarker. The screening, identification, and analysis of these four genes could contribute to the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.

Comprehensive analysis of immune infiltration and gene expression for predicting survival in patients with sarcomas

Tumor microenvironments are strongly related to tumor development, and immune-infiltrating cells and immune-related molecules are potential prognostic markers. However, the shortcomings of traditional measurement methods limit the accurate evaluation of various components in tumor microenvironments. With the rapid advancement of Next-Generation RNA Sequencing technology, dedicated and in-depth analyses of immune filtration within the tumor microenvironment has been achieved. In this study, we combined the bioinformatics analysis methods ESTIMATE, CIBERSORT, and ssGSEA to characterize the immune infiltration of sarcomas and to identify specific immunomodulators of different pathological subtypes. We further extracted a functional enrichment of significant immune-related genes related to improved prognosis, including NR1H3, VAMP5, GIMAP2, GBP2, HLA-E and CRIP1. Overall, the immune microenvironment is an important prognostic determinant of sarcomas and may be a potential resource for developing effective immunotherapy.

Identification of purity and prognosis-related gene signature by network analysis and survival analysis in brain lower grade glioma

Tumour microenvironment of brain lower grade glioma (LGG) consists of non‐tumour cells including stromal cells and immune cells mainly. These non‐tumour cells dilute the purity of LGG and play pivotal roles in tumour growth and development, thereby affecting patient prognosis. Tumour purity is also associated with molecular subtypes of LGG. In this study, we discovered the most relevant module to purity by weighted gene co‐expression network analysis (WGCNA) and afterwards performed consensus network analysis and survival analysis to filter 61 significant genes related to both purity and prognosis. In turn, we built a simplified model based on the calculation of purity score, and consensus measurement of purity estimation (CPE), with a satisfactory predictive performance by random forest regression. HLA‐E, MSN, GNG‐5, MYL12A, ITGB4, PDPN, AGTRAP, S100A4, PLSCR1, VAMP5 were selected as the most relevant genes correlating to both purity and prognosis. The risk score model based on the 10 genes could moderately predict patients’ overall survival. These 10 genes, respectively, were positively correlated positively to immunosuppressive cells like macrophage M2, but negatively correlated to patient prognosis, which may explain partially the poor prognosis with low‐purity group.