A novel DNA damage and repair‐related gene signature to improve predictive capacity of overall survival for patients with gliomas

Gliomas, as the most lethal and malignant brain tumours in adults, remain a major challenge worldwide. DNA damage and repair‐related genes (DDRRGs) appear to play a significant role in gliomas, but the studies of DDRRGs are still insufficient. Herein, we systematically explored and analysed 1547 DDRRGs in 938 glioma samples from TCGA and CGGA datasets. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a 16‐DDRRG signature, characterized by high‐risk and low‐risk patterns. This risk model harbours robust predictive capability for overall survival of glioma patients. We found the high‐risk score is strongly associated with well‐known malignant features of gliomas, such as the mesenchymal subtype, IDH‐wildtype, 1p/19q non‐codeletion and MGMT promoter unmethylated status. In addition, we found that the high‐risk score is also linked with multiple oncogenic pathways and therapeutic resistance. Significantly, we found the high‐risk group has higher enrichment of immunosuppressive cells (M2‐type macrophages, Tregs and MDSCs) and immune inhibition biomarkers (PD‐1, PD‐L1 and CTLA‐4). Lastly, we proved that SMC4, which has the highest positive regression coefficient in our risk model, is strongly linked with malignant progression and TMZ resistance of gliomas in a E2F1‐dependent manner.     

Pyroptosis‐related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.     

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.     

M2‐phenotype tumour‐associated macrophages upregulate the expression of prognostic predictors MMP14 and INHBA in pancreatic cancer

Pancreatic cancer is one of the most lethal gastrointestinal tumours, the most common pathological type is pancreatic adenocarcinoma (PAAD). In recent year, immune imbalanced in tumour microenvironment has been shown to play an important role in the evolution of tumours progression, and the efficacy of immunotherapy has been gradually demonstrated in clinical practice. In this study, we propose to construct an immune‐related prognostic risk model based on immune‐related genes MMP14 and INHBA expression that can assess the prognosis of pancreatic cancer patients and identify potential therapeutic targets for pancreatic cancer, to provide new ideas for the treatment of pancreatic cancer. We also investigate the correlation between macrophage infiltration and MMP14 and INHBA expression. First, the gene expression data of pancreatic cancer and normal pancreatic tissue were obtained from The Cancer Genome Atlas Program (TCGA) and The Genotype‐Tissue Expression public database (GTEx). The differentially expressed immune‐related genes between pancreatic cancer samples and normal sample were screened by R software. Secondly, univariate Cox regression analysis were used to evaluate the relationship between immune‐related genes and the prognosis of pancreatic cancer patients. A polygenic risk score model was constructed by Cox regression analysis. The prognostic nomogram was constructed, and its performance was evaluated comprehensively by internal calibration curve and C‐index. Using the risk model, each patient gets a risk score, and was divided into high‐ or low‐ risk groups. The proportion of 22 types of immune cells infiltration in pancreatic cancer samples was inferred by CIBERSOFT algorithm, correlation analysis (Pearson method) was used to analyse the correlation between the immune‐related genes and immunes cells. Then, we applied macrophage conditioned medium to culture pancreatic cancer cell line PANC1, detected the expression of MMP14 and INHBA by qRT‐PCR and Western blot methods. Knock‐down MMP14 and INHBA in PANC1 cells by transfected with shRNA lentiviruses. Detection of migration ability of pancreatic cells was done by trans‐well cell migration assay. A subcutaneous xenograft tumour model of human pancreatic cancer in nude mice was constructed. In conclusion, an immune‐related gene prognostic model was constructed, patients with high‐risk scores have poorer survival status, M2‐phenotype tumour‐associated macrophages (TAMs) up‐regulate two immune‐related genes, MMP14 and INHBA, which were used to establish the prognostic model. Knock‐down of MMP14 and INHBA inhibited invasion of pancreatic cancer.     

Prognostic value and immune cell infiltration of hypoxic phenotype‐related gene signatures in glioblastoma microenvironment

Glioblastoma (GBM) is a malignant intracranial tumour with the highest proportion and lethality. It is characterized by invasiveness and heterogeneity. However, the currently available therapies are not curative. As an essential environmental cue that maintains glioma stem cells, hypoxia is considered the cause of tumour resistance to chemotherapy and radiation. Growing evidence shows that immunotherapy focusing on the tumour microenvironment is an effective treatment for GBM; however, the current clinicopathological features cannot predict the response to immunotherapy and provide accurate guidance for immunotherapy. Based on the ESTIMATE algorithm, GBM cases of The Cancer Genome Atlas (TCGA) data set were classified into high‐ and low‐immune/stromal score groups, and a four‐gene tumour environment‐related model was constructed. This model exhibited good efficiency at forecasting short‐ and long‐term prognosis and could also act as an independent prognostic biomarker. Additionally, this model and four of its genes (CLECL5A, SERPING1, CHI3L1 and C1R) were found to be associated with immune cell infiltration, and further study demonstrated that these four genes might drive the hypoxic phenotype of perinecrotic GBM, which affects hypoxia‐induced glioma stemness. Therefore, these might be important candidates for immunotherapy of GBM and deserve further exploration.     

Circadian clock genes promote glioma progression by affecting tumour immune infiltration and tumour cell proliferation

ObjectivesCircadian rhythm controls complicated physiological activities in organisms. Circadian clock genes have been related to tumour progression, but its role in glioma is unknown. Therefore, we explored the relationship between dysregulated circadian clock genes and glioma progression.Materials and MethodsSamples were divided into different groups based on circadian clock gene expression in training dataset (n = 672) and we verified the results in other four validating datasets (n = 1570). The GO and GSEA enrichment analysis were conducted to explore potential mechanism of how circadian clock genes affected glioma progression. The single‐cell RNA‐Seq analysis was conducted to verified previous results. The immune landscape was evaluated by the ssGSEA and CIBERSORT algorithm. Cell proliferation and viability were confirmed by the CCK8 assay, colony‐forming assay and flow cytometry.ResultsThe cluster and risk model based on circadian clock gene expression can predict survival outcome. Samples were scoring by the least absolute shrinkage and selection operator regression analysis, and high scoring tumour was associated with worse survival outcome. Samples in high‐risk group manifested higher activation of immune pathway and cell cycle. Tumour immune landscape suggested high‐risk tumour infiltrated more immunocytes and more sensitivity to immunotherapy. Interfering TIMELESS expression affected circadian clock gene expression, inhibited tumour cell proliferation and arrested cell cycle at the G0/G1 phase.ConclusionsDysregulated circadian clock gene expression can affect glioma progression by affecting tumour immune landscape and cell cycle. The risk model can predict glioma survival outcome, and this model can also be applied to pan‐cancer.     

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.     

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.     

Prediction of a competing endogenous RNA co‐expression network as a prognostic marker in glioblastoma

Due to its high proliferation capacity and rapid intracranial spread, glioblastoma (GBM) has become one of the least curable malignant cancers. Recently, the competing endogenous RNAs (ceRNAs) hypothesis has become a focus in the researches of molecular biological mechanisms of cancer occurrence and progression. However, there is a lack of correlation studies on GBM, as well as a lack of comprehensive analyses of GBM molecular mechanisms based on high‐throughput sequencing and large‐scale sample sizes. We obtained RNA‐seq data from The Cancer Genome Atlas (TCGA) and Genotype‐Tissue Expression (GTEx) databases. Further, differentially expressed mRNAs were identified from normal brain tissue and GBM tissue. The similarities between the mRNA modules with clinical traits were subjected to weighted correlation network analysis (WGCNA). With the mRNAs from clinical‐related modules, a survival model was constructed by univariate and multivariate Cox proportional hazard regression analyses. Thereafter, we carried out Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, we predicted interactions between lncRNAs, miRNAs and mRNAs by TargetScan, miRDB, miRTarBase and starBase. We identified 2 lncRNAs (NORAD, XIST), 5 miRNAs (hsa‐miR‐3613, hsa‐miR‐371, hsa‐miR‐373, hsa‐miR‐32, hsa‐miR‐92) and 2 mRNAs (LYZ, PIK3AP1) for the construction of a ceRNA network, which might act as a prognostic biomarker of GBM. Combined with previous studies and our enrichment analysis results, we hypothesized that this ceRNA network affects immune activities and tumour microenvironment variations. Our research provides novel aspects to study GBM development and treatment.     

Predicting the prognosis of glioma by pyroptosis‐related signature

Glioma is the most common malignant primary brain tumour. It is of great significance for the prognosis and personalized treatment of glioma patients to accurate identification of glioma based on biomarkers. Pyroptosis, a kind of programmed cell death, is closely related to tumour progression and tumour immune microenvironment. However, the role of pyroptosis in glioma remained unclear. Herein, we used glioma clinical and expression data from TCGA and CGGA to explore the relationship between pyroptosis and glioma. We first summarized the incidence of copy number variations and somatic mutations of 33 pyroptosis‐related genes and explored prognostic correlation of these genes. Based on pyroptosis‐related genes, three molecular subgroups of glioma related to prognosis were identified. We also found that each subgroup has unique immune and biological behaviours characteristics. Finally, based on 7 pyroptosis‐related genes (CASP3, CASP4, CASP6, CASP8, CASP9, PRKACA and ELANE), we constructed a prognosis model by Lasso and Cox regression, which had a strong predictive power for the overall survival in CGGA test cohort (p < 0.05). In summary, we explored the role of pyroptosis‐related genes in gliomas and the association of these genes with tumour immunity. We found the biomarkers valuable to diagnosis and prognosis, hence, provide reference to the development and treatment of tumorigenesis in glioma.     

Characterization and prognostic significance of alternative splicing events in lower‐grade diffuse gliomas

Alternative splicing (AS) is assumed to play important roles in the progression and prognosis of cancer. Currently, the comprehensive analysis and clinical relevance of AS in lower‐grade diffuse gliomas have not been systematically addressed. Here, we gathered alternative splicing data of lower‐grade diffuse gliomas from SpliceSeq. Based on the Percent Spliced In (PSI) values of 515 lower‐grade diffuse glioma patients from the Cancer Genome Atlas (TCGA), we performed subtype‐differential AS analysis and consensus clustering to determine robust clusters of patients. A total of 48 050 AS events in 10 787 genes in lower‐grade diffuse gliomas were profiled. Subtype‐differential splicing analysis and functional annotation revealed that spliced genes were significantly enriched in numerous cancer‐related biological phenotypes and signalling pathways. Consensus clustering using AS events identified three robust clusters of patients with distinguished pathological and prognostic features. Moreover, each cluster was also associated with distinct genomic alterations. Finally, we developed and validated an AS‐related signature with Cox proportional hazards model. The signature, significantly associated with clinical and molecular features, could serve as an independent prognostic factor for lower‐grade diffuse gliomas. Thus, our results indicated that AS events could discriminate molecular subtypes and have prognostic impact in lower‐grade diffuse gliomas.     

Establishment of a five‐enzalutamide‐resistance‐related‐gene‐based classifier for recurrence‐free survival predicting of prostate cancer

To identify prostate cancer (PCa) patients with a high risk of recurrence is critical before delivering adjuvant treatment. We developed a classifier based on the Enzalutamide treatment resistance‐related genes to assist the currently available staging system in predicting the recurrence‐free survival (RFS) prognosis of PCa patients. We overlapped the DEGs from two datasets to obtain a more convincing Enzalutamide‐resistance‐related‐gene (ERRG) cluster. The five‐ERRG‐based classifier obtained good predictive values in both the training and validation cohorts. The classifier precisely predicted RFS of patients in four cohorts, independent of patient age, pathological tumour stage, Gleason score and PSA levels. The classifier and the clinicopathological factors were combined to construct a nomogram, which had an increased predictive accuracy than that of each variable alone. Besides, we also compared the differences between high‐ and low‐risk subgroups and found their differences were enriched in cancer progression‐related pathways. The five‐ERRG‐based classifier is a practical and reliable predictor, which adds value to the existing staging system for predicting the RFS prognosis of PCa after radical prostatectomy, enabling physicians to make more informed treatment decisions concerning adjuvant therapy.     

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.     

Clinical and prognostic implications of an immune‐related risk model based on TP53 status in lung adenocarcinoma

TP53 mutation is the most widespread mutation in lung adenocarcinoma (LUAD). Meanwhile, p53 (encoded by TP53) has recently been implicated in immune responses. However, it is still unknown whether TP53 mutation remodels the tumour microenvironment to influence tumour progression and prognosis in LUAD. In this study, we developed a 6‐gene immune‐related risk model (IRM) to predict the survival of patients with LUAD in The Cancer Genome Atlas (TCGA) cohort based on TP53 status, and the predictive ability was confirmed in 2 independent cohorts. TP53 mutation led to a decreased immune response in LUAD. Further analysis revealed that patients in the high‐index group had observably lower relative infiltration of memory B cells and regulatory T cells and significantly higher relative infiltration of neutrophils and resting memory CD4⁺ T cells. Additionally, the IRM index positively correlated with the expression of critical immune checkpoint genes, including PDCD1 (encoding PD‐1) and CD274 (encoding PD‐L1), which was validated in the Nanjing cohort. Furthermore, as an independent prognostic factor, the IRM index was used to establish a nomogram for clinical application. In conclusion, this IRM may serve as a powerful prognostic tool to further optimize LUAD immunotherapy.     

High‐altitude adaptation in vertebrates as revealed by mitochondrial genome analyses

The high‐altitude environment may drive vertebrate evolution in a certain way, and vertebrates living in different altitude environments might have different energy requirements. We hypothesized that the high‐altitude environment might impose different influences on vertebrate mitochondrial genomes (mtDNA). We used selection pressure analyses and PIC (phylogenetic independent contrasts) analysis to detect the evolutionary rate of vertebrate mtDNA protein‐coding genes (PCGs) from different altitudes. The results showed that the ratio of nonsynonymous/synonymous substitutions (dN/dS) in the mtDNA PCGs was significantly higher in high‐altitude vertebrates than in low‐altitude vertebrates. The seven rapidly evolving genes were shared by the high‐altitude vertebrates, and only one positive selection gene (ND5 gene) was detected in the high‐altitude vertebrates. Our results suggest the mtDNA evolutionary rate in high‐altitude vertebrates was higher than in low‐altitude vertebrates as their evolution requires more energy in a high‐altitude environment. Our study demonstrates the high‐altitude environment (low atmospheric O₂ levels) drives vertebrate evolution in mtDNA PCGs.     

Sildenafil improves radiation‐induced oral mucositis by attenuating oxidative stress,NF‐κB,ERK and JNK signalling pathways

Radiation‐induced oral mucositis is a common and dose‐limiting complication of head and neck radiotherapy with no effective treatment. Previous studies revealed that sildenafil, a phosphodiesterase 5 inhibitor, has anti‐inflammatory and anti‐cancer effects. In this study, we investigated the effect of sildenafil on radiation‐induced mucositis in rats. Two doses of radiation (8 and 26 Gy X‐ray) were used to induce low‐grade and high‐grade oral mucositis, separately. A control group and three groups of sildenafil citrate‐treated rats (5, 10, and 40 mg/kg/day) were used for each dose of radiation. Radiation increased MDA and activated NF‐κB, ERK and JNK signalling pathways. Sildenafil significantly decreased MDA level, nitric oxide (NO) level, IL1β, IL6 and TNF‐α. The most effective dose of sildenafil was 40 mg/kg/day in this study. Sildenafil also significantly inhibited NF‐κB, ERK and JNK signalling pathways and increased bcl2/bax ratio. In addition, high‐dose radiation severely destructed the mucosal layer in histopathology and led to mucosal cell apoptosis in the TUNEL assay. Sildenafil significantly improved mucosal structure and decreased inflammatory cell infiltration after exposure to high‐dose radiation and reduced apoptosis in the TUNEL assay. These findings show that sildenafil can improve radiation‐induced oral mucositis and decrease the apoptosis of mucosal cells via attenuation of inflammation and oxidative stress.     

Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.