Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is associated with high mortality rate. However, the correlation between immune microenvironment and prognosis of OC remains unclear. This study aimed to explore prognostic significance of OC tumour microenvironment. The OC data set was selected from the cancer genome atlas (TCGA), and 307 samples were collected. Hierarchical clustering was performed according to the expression of 756 genes. The immune and matrix scores of all immune subtypes were determined, and Kruskal-Wallis test was used to analyse the differences in the immune and matrix scores between OC samples with different immune subtypes. The model for predicting prognosis was constructed based on the expression of immune-related genes. TIDE platform was applied to predict the effect of immunotherapy on patients with OC of different immune subtypes. The 307 OC samples were classified into three immune subtypes A-C. Patients in subtype B had poorer prognosis and lower survival rate. The infiltration of helper T cells and macrophages in microenvironment indicated significant differences between immune subtypes. Enrichment analyses of immune cell molecular pathways showed that JAK–STAT3 pathway changed significantly in subtype B. Furthermore, predictive response to immunotherapy in subtype B was significantly higher than that in subtype A and C. Immune subtyping can be used as an independent predictor of the prognosis of OC patients, which may be related to the infiltration patterns of immune cells in tumour microenvironment. In addition, patients in immune subtype B have superior response to immunotherapy, suggesting that patients in subtype B are suitable for immunotherapy.     

Increased microglia/macrophage gene expression in a subset of adult and pediatric astrocytomas

Glioblastoma (GBM) is a highly malignant brain tumor with a dismal prognosis. Gene expression profiling of GBM has revealed clinically relevant tumor subtypes, and this provides exciting opportunities to better understand disease pathogenesis. Results from an increasing number of studies demonstrate a role for the immune response in cancer progression, yet it is unclear how the immune response differs across tumor subtypes and how it affects outcome. Utilizing gene expression data from The Cancer Genome Atlas Project and the Gene Expression Omnibus database, we demonstrate an enrichment of immune response-related gene expression in the mesenchymal subtype of adult GBM (n = 173) and pediatric high-grade gliomas (n = 53). In an independent cohort of pediatric astrocytomas (n = 24) from UCSF, we stratified tumors into subtypes and confirmed these findings. Using novel immune cell-specific gene signatures we demonstrate selective enrichment of microglia/macrophage-related genes in adult and pediatric GBM tumors of the mesenchymal subtype. Furthermore, immunostaining of adult GBM tumors showed significantly higher cell numbers of microglia/macrophages in mesenchymal versus non-mesenchymal tumors (p = 0.04). Interestingly, adult GBM tumors with the shortest survival had significant enrichment of microglia/macrophage-related genes but this was not true for pediatric GBMs. Consistent with an association with poor outcome, immune response-related genes were highly represented in an adult poor prognosis gene signature, with the expression of genes related to macrophage recruitment and activation being most strongly associated with survival (p<0.05) using CoxBoost multivariate modeling. Using a microglia/macrophage high gene signature derived from quantification of tumor-infiltrating cells in adult GBM, we identified enrichment of genes characteristic of CD4 T cells, granulocytes, and microglia/macrophages (n = 573). These studies support a role for the immune response, particularly the microglia/macrophage response, in the biology of an important subset of GBM. Identification of this subset may be important for future therapeutic stratification.     

AKT Pathway Genes Define 5 Prognostic Subgroups in Glioblastoma

Activity of GFR/PI3K/AKT pathway inhibitors in glioblastoma clinical trials has not been robust. We hypothesized variations in the pathway between tumors contribute to poor response. We clustered GBM based on AKT pathway genes and discovered new subtypes then characterized their clinical and molecular features. There are at least 5 GBM AKT subtypes having distinct DNA copy number alterations, enrichment in oncogenes and tumor suppressor genes and patterns of expression for PI3K/AKT/mTOR signaling components. Gene Ontology terms indicate a different cell of origin or dominant phenotype for each subgroup. Evidence suggests one subtype is very sensitive to BCNU or CCNU (median survival 5.8 vs. 1.5 years; BCNU/CCNU vs other treatments; respectively). AKT subtyping advances previous approaches by revealing additional subgroups with unique clinical and molecular features. Evidence indicates it is a predictive marker for response to BCNU or CCNU and PI3K/AKT/mTOR pathway inhibitors. We anticipate Akt subtyping may help stratify patients for clinical trials and augment discovery of class-specific therapeutic targets.     

Transcriptome analyses reveal molecular mechanisms underlying phenotypic differences among transcriptional subtypes of glioblastoma

Using molecular signatures, previous studies have defined glioblastoma (GBM) subtypes with different phenotypes, such as the proneural (PN), neural (NL), mesenchymal (MES) and classical (CL) subtypes. However, the gene programmes underlying the phenotypes of these subtypes were less known. We applied weighted gene co-expression network analysis to establish gene modules corresponding to various subtypes. RNA-seq and immunohistochemical data were used to validate the expression of identified genes. We identified seven molecular subtype-specific modules and several candidate signature genes for different subtypes. Next, we revealed, for the first time, that radioresistant/chemoresistant gene signatures exist only in the PN subtype, as described by Verhaak et al, but do not exist in the PN subtype described by Phillips et al PN subtype. Moreover, we revealed that the tumour cells in the MES subtype GBMs are under ER stress and that angiogenesis and the immune inflammatory response are both significantly elevated in this subtype. The molecular basis of these biological processes was also uncovered. Genes associated with alternative RNA splicing are up-regulated in the CL subtype GBMs, and genes pertaining to energy synthesis are elevated in the NL subtype GBMs. In addition, we identified several survival-associated genes that positively correlated with glioma grades. The identified intrinsic characteristics of different GBM subtypes can offer a potential clue to the pathogenesis and possible therapeutic targets for various subtypes.     

Pyroptosis in glioblastoma: A crucial regulator of the tumour immune microenvironment and a predictor of prognosis

Recent studies have shown that pyroptosis, an inflammatory form of cell death, has a dual role in tumorigenesis and tumour progression and affects the prognosis of patients; however, the role of pyroptosis in glioblastoma (GBM) is still unclear. In this study, based on GBM patients'' data from two independent cohorts, we performed a comprehensive analysis of the expression and prognostic value of 33 pyroptosis‐associated genes (PAGs) in GBM, as well as their role in the tumour immune microenvironment (TIME) of GBM. We identified 29 PAGs that were differentially expressed between GBM and normal brain tissue, 18 of which were upregulated in GBM tissue. Most of the 33 PAGs were strongly correlated with the levels of immune cell infiltration. Based on the 33 PAGs, the GBM samples can be divided into two clusters (C1‐C2), with C1 having a ‘hot’ but immunosuppressive TIME and C2 having a ‘cold’ TIME, suggesting different immunotherapeutic responses in the two clusters. In addition, we identified four PAGs that were strongly associated with GBM prognosis and constructed a risk model based on these four PAGs. This risk model is an independent prognostic factor for GBM patients, and there is a different immune status between high‐ and low‐risk groups. In conclusion, this study demonstrates that pyroptosis is closely associated with the prognosis and TIME of GBM and provides an important basis for further studies on the relationship between pyroptosis and GBM.     

Expression signature of IFN/STAT1 signaling genes predicts poor survival outcome in glioblastoma multiforme in a subtype-specific manner

Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.     

Clinical and molecular characteristics of bladder urothelial carcinoma subtypes

Bladder urothelial carcinoma (BLCA) is a common malignancy with high heterogeneity. A reasonable molecular subtyping can facilitate biological study and personalized therapy of BLCA. In this study, unsupervised consensus clustering was used to acquire the molecular subtypes of BLCA based on messenger RNA (mRNA) and microRNA (miRNA) data. Gene signature markers and canonical signaling pathways were compared between different subtypes. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for the functional annotation of overexpressed genes in different subtypes for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Three molecular subtypes were identified including C1 (luminal-P53 like), C2 (luminal-other), and C3 (basal-immune-squamous). C2 was different from C1 and C3 in clinical characteristics, including younger, better prognosis, and a higher proportion of papillary, Asian, low-grade, early-stage, lymph node negative, and complete remission patients (P < 0.05). Three molecular subtypes also showed distinct mRNA and miRNA expression patterns. luminal and P53-like markers were highly expressed in subtype C1, luminal markers were highly expressed in subtype C2, and basal, EMT/claudin-low, immune and squamous-differentiation markers were highly expressed in subtype C3. In addition, highly expressed genes in C1 were involved in extracellular signal transduction and cell-cell interaction, highly expressed genes in C2 were associated with oxygen transport, energy and steroid metabolism, and highly expressed genes in C3 were related with inflammatory, immune, cytokine, and signal transduction. BLCA in different molecular subtypes showed different clinical and molecular characteristics and personalized therapy needed to be adopted according to the molecular subtypes.     

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM.     

A glycolysis-based ten-gene signature correlates with the clinical outcome,molecular subtype and IDH1 mutation in glioblastoma

Reprogrammed metabolism is a hallmark of cancer. Glioblastoma (GBM) tumor cells predominantly utilize aerobic glycolysis for the biogenesis of energy and intermediate nutrients. However, in GBM, the clinical significance of glycolysis and its underlying relations with the molecular features such as IDH1 mutation and subtype have not been elucidated yet. Herein, based on glioma datasets including TCGA (The Cancer Genome Atlas), REMBRANDT (Repository for Molecular Brain Neoplasia Data) and GSE16011, we established a glycolytic gene expression signature score (GGESS) by incorporating ten glycolytic genes. Then we performed survival analyses and investigated the correlations between GGESS and IDH1 mutation as well as the molecular subtypes in GBMs. The results showed that GGESS independently predicted unfavorable prognosis and poor response to chemotherapy of GBM patients. Notably, GGESS was high in GBMs of mesenchymal subtype but low in IDH1-mutant GBMs. Furthermore, we found that the promoter regions of tumor-promoting glycolytic genes were hypermethylated in IDH1-mutant GBMs. Finally, we found that high GGESS also predicted poor prognosis and poor response to chemotherapy when investigating IDH1-wildtype GBM patients only. Collectively, glycolysis represented by GGESS predicts unfavorable clinical outcome of GBM patients and is closely associated with mesenchymal subtype and IDH1 mutation in GBMs.     

Identification of immune-enhanced molecular subtype associated with BRCA1 mutations,immune checkpoints and clinical outcome in ovarian carcinoma

Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune-related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour-infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.     

Identification of metabolism-associated molecular subtype in ovarian cancer

Ovarian cancer (OC) is the most lethal gynaecological cancer with genomic complexity and extensive heterogeneity. This study aimed to characterize the molecular features of OC based on the gene expression profile of 2752 previously characterized metabolism-relevant genes and provide new strategies to improve the clinical status of patients with OC. Finally, three molecular subtypes (C1, C2 and C3) were identified. The C2 subtype displayed the worst prognosis, upregulated immune-cell infiltration status and expression level of immune checkpoint genes, lower burden of copy number gains and losses and suboptimal response to targeted drug bevacizumab. The C1 subtype showed downregulated immune-cell infiltration status and expression level of immune checkpoint genes, the lowest incidence of BRCA mutation and optimal response to targeted drug bevacizumab. The C3 subtype had an intermediate immune status, the highest incidence of BRCA mutation and a secondary optimal response to bevacizumab. Gene signatures of C1 and C2 subtypes with an opposite expression level were mainly enriched in proteolysis and immune-related biological process. The C3 subtype was mainly enriched in the T cell-related biological process. The prognostic and immune status of subtypes were validated in the Gene Expression Omnibus (GEO) dataset, which was predicted with a 45-gene classifier. These findings might improve the understanding of the diversity and therapeutic strategies for OC.     

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso-Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.     

Identification and Characterization of Alcohol-related Hepatocellular Carcinoma Prognostic Subtypes based on an Integrative N6-methyladenosine methylation Model

Background: Alcohol consumption increases the risk of hepatocellular carcinoma (HCC), and associated with a high mortality rate and poor prognosis. N6-methyladenosine (m6A) methylations play key roles in tumorigenesis and progression. However, our current knowledge about m6A in alcohol-related HCC (A-HCC) remains elucidated. Herein, the authors construct an integrative m6A model based on A-HCC subtyping and mechanism exploration workflow.Methods: Based on the m6A expressions of A-HCC and in vivo experiment, different prognosis risk A-HCC subtypes are identified. Meanwhile, multiple interdependent indicators of prognosis including patient survival rate, clinical pathological prognosis and immunotherapy sensitivity.Results: The m6A model includes LRPPRC, YTHDF2, KIAA14219, and RBM15B, classified A-HCC patients into high/low-risk subtypes. The high-risk subtype compared to the low-risk subtype showed phenotypic malignancy, poor prognosis, immunosuppression, and activation of tumorigenesis and proliferation-related pathways, including the E2F target, DNA repair, and mTORC1 signalling pathways. The expression of Immunosuppressive cytokines DNMT1/EZH2 was up-regulated in A-HCC patients, and teniposide may be a potential therapeutic drug for A-HCC.Conclusion: Our model redefined A-HCC prognosis risk, identified potential m6As linking tumour progress and immune regulations and selected possible therapy target, thus promoting understanding and clinical applications about A-HCC.     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

Suppression of G-protein-coupled receptor kinase 3 expression is a feature of classical GBM that is required for maximal growth

G-protein-coupled receptor kinases (GRK) regulate the function of G-protein-coupled receptors (GPCR). Previously, we found that GPCR (CXCR4)-mediated astrocytoma growth was dependent upon abnormally sustained CXCR4 signaling and was correlated with decreased GRK-mediated receptor phosphorylation. As CXCR4 has also been implicated in the stimulation of high-grade glioma growth, we sought to determine whether dysregulation of GRK expression and/or function might also be present in high-grade gliomas. In an analysis of data from The Cancer Genome Atlas, we found that GRK3 expression is frequently decreased in glioblastoma (GBM) of the classical subtype, which possesses signature amplification or mutational activation of the epidermal growth factor (EGF) receptor. We tested the correlation between GRK3 expression and GBM subtypes, as well as the relationship between the activation of the EGF and other growth factor receptor pathways and GRK expression. In analyses of primary GBM tissue and RNA specimens, we found that GRK3 expression is correlated with established criteria for GBM subtyping including expression of EGF receptor, platelet-derived growth factor receptor (PDGFR)α, NF1, PTEN, CDKN2A, and neurofilament. We also found that established drivers of gliomagenesis, the EGF, PDGF, and TGF-β pathways, all regulate GRK expression. Coculture experiments, designed to mimic critical interactions between tumor and brain microvascular endothelial cells, showed that specifically increasing GRK3 expression reduced the trophic effect of endothelial cells on tumor cells. Together, these experiments show that GRK3 is a negative regulator of cell growth whose expression is preferentially reduced in GBM of the classical subtype as a consequence of activity in primary gliomagenic pathways.     

Identification of necroptosis‐related gene signature and characterization of tumour microenvironment infiltration in non‐small‐cell lung cancer

Necroptosis is a programmed necrosis in a caspase‐independent fashion. The role of necroptosis‐related genes (NRGs) in lung cancer remains unknow. Herein, we classified TCGA‐LUAD cohort into two necroptosis‐related subtypes (C1 and C2) by consensus clustering analysis. The result showed that subtype C1 had a favourable prognosis and higher infiltration levels of immune cells. Moreover, subtype C1 was more activated in immune‐associated pathways. Then, we established an NRG prognosis model (NRG score) composed of six NRGs (RIPK3, MLKL, TLR2, TLR4, TNFRSF1A, NDRG2) and divided the cohort into low‐ and high‐risk group. We found that the NRG score was associated with prognosis, tumour immune microenvironment and tumour mutation burden. We also constructed an accurate nomogram model to improve the clinical applicability of NRG score. The result indicated that NRG score may be an independent prognostic marker for lung cancer patients. Taken together, we established a prognosis model that may deepen the understanding of NRGs in lung cancer and provide a basis for developing more effective immunotherapy strategies.