Prognostic value of a microRNA signature as a novel biomarker in patients with lower-grade gliomas

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC

BACKGROUND

As one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown.

METHODS

The differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features.

RESULTS

In our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs.

CONCLUSIONS

The new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Identification of immunity- and ferroptosis-related genes for predicting the prognosis of serous ovarian cancer

BACKGROUND

Serous ovarian cancer (SOC) is the most common type of ovarian cancer (OC), with bad outcomes. To improve the prognosis of SOC patients, a novel risk signature was developed by combining immunity- and ferroptosis-related genes.

METHODS

By means of comparing SOC tissues with normal tissues, we screened the differential expression of immunity-related genes (DE-IRGs) and ferroptosis-related genes(DE-FRGs) with the standards of |log₂fold change| > 1 and false discovery rate (FDR) < 0.05. After obtaining the meaningful differentially expressed genes from immune and ferroptosis (DEGs), we established a prognostic risk signature by utilizing Cox regression analyses in TCGA training set, which was validated in TCGA testing set and GSE26712 dataset. Besides, the differential expression of immune-related markers, immunophenoscore (IPS), TIDE score,T cell dysfunction score and T cell exclusion score were also analyzed. We further verified the expression of target genes in ovarian tumor cells lines by QRT-PCR.

RESULTS

A risk signature constructed by totally four immunity- and ferroptosis-related DEGs (CXCL11, CX3CR1, FH, and DNAJB6) was developed, which distinguished the SOC patients as high-risk and low-risk groups. Patients in the high-risk group showed a lower overall survival (OS) than those in the low-risk group. Furthermore, the risk score was independent when analyzed with clinical augments, which was significantly associated with 13 KEGG signaling pathways. The gene signature showed favorable predictive performance according to Receiver operating characteristic (ROC) curves. Notably, the expression of immune-related markers or IPS indicated a negative connection with the risk score. SOC patients had a lower score of TIDE and T cell dysfunction than Whom had a higher score. Nonetheless, there were no significant differences in T cell exclusion scores between the two groups.Compared with normal ovarian cell line IOSE-80,QRT-PCR experiments exhibited that CXCL11, CX3CR1and FH were up-regulated in ovarian tumor cells lines(SK-OV-3,COC1,A2780),while DNAJB6 was down-regulated.

CONCLUSION

Four-biomarker signature formed by immunity- and ferroptosis-related genes may be clinically used as risk stratifcation tool in serous ovarian cancer,which can help further clinical decision-making regarding prognostic prediction,individualized treatment and follow-up scheduling.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

BACKGROUND

Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.

METHODS

Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.

FINDINGS

APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.

INTERPRETATION

APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).

FUNDING

National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

Construction of a novel microRNA-based signature for predicting the prognosis of glioma

Background and purpose: Glioma is a frequent primary brain tumor. MicroRNAs (miRNA) have been shown to potentially play a crucial part in tumor development. Based on miRNAs and clinical factors, a model was constructed to predict the glioma prognosis. Methods: The miRNA expression profiles of glioma come from The Cancer Genome Atlas (TCGA, training group) and Chinese Glioma Genome Atlas (CGGA, validation group). Regression analyses of Cox and Lasso were applied to identity miRNAs associated with glioma prognosis in the TCGA database. The miRNAs were combined with clinical factors to construct individualized prognostic prediction models, whose performance was validated in the CGGA database. The role of miRNA in glioma development was investigated by in vitro experiments.Results: We identified five key miRNAs associated with glioma prognosis and constructed a prediction model. The area under ROC curve for predicting 3-year survival of glioma patients in the TCGA and CGGA groups was 0.844 and 0.770, respectively. The nomogram constructed using the miRNA risk scores and clinical factors showed high accuracy of prediction in the TCGA group (C-index of 0.820) and the CGGA group (C-index of 0.722). The miR-196b-5p altered the migration, proliferation, invasion, and apoptosis of glioma cells by regulating target genes, according to in vitro experiments.Conclusions: A miRNA-based individualized prognostic prediction model was constructed for glioma and miR-196b-5p was identified as a potential biomarker of glioma development.

Development and Validation of a Novel Prognostic Model for Lower-Grade Glioma Based on Enhancer RNA-Regulated Prognostic Genes

Enhancer RNAs (eRNAs) are present specifically in tumors, where they affect the expression of eRNA-regulated genes (ERGs). Owing to this characteristic, ERGs were hypothesized to improve prognosis of overall survival in heterogeneous low-grade and intermediate-grade gliomas. This study aimed to construct and validate an ERG prognostic tool to facilitate clinical management, and offer more effective diagnostic and therapeutic biomarkers for glioma. Survival-related eRNAs were identified, and their ERGs were selected based on eRNA and target gene information. The ERG prognostic model was constructed and validated using internal and external validation cohorts. Finally, biological differences related to the ERG signature were analysed to explore the potential mechanisms influencing survival outcomes. Thirteen ERGs were identified and used to build an ERG risk signature, which included five super-enhancer RNA (seRNA)-regulated genes and five LGG-specific eRNA-regulated genes. The prognostic nomogram established based on combining the ERG score, age, and sex was evaluated by calibration curves, clinical utility, Harrell’s concordance index (0.86; 95% CI: 0.83-0.90), and time-dependent receiver operator characteristic curves. We also explored potential immune-related mechanisms that might cause variation in survival. The established prognostic model displayed high validity and robustness. Several immune-related genes regulated by seRNAs or specific eRNAs were identified, indicating that these transcripts or their genes were potential targets for improving immunotherapeutic/therapeutic outcomes. The functions of an important specific eRNA-regulated gene (USP28) were validated in robust vitro experiments. In addition, the ERG risk signature was significantly associated with the immune microenvironment and other immune-related features.

A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas

Background. Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS. Methods. Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified. Results. A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS ( $\text{AUC}=0.930$ , 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability ( $\text{C}-\text{index}=0.828$ ). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts. Conclusion. IRSS may serve as a novel survival prediction tool in the classification of LGG patients.

A 7-gene signature predicts the prognosis of patients with bladder cancer

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C‐index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693–0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

A Novel Prognostic Tool for Glioma Based on Enhancer RNA-Regulated Immune Genes

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized.

Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas.

Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell’s concordance index (0.87; 95% CI: 0.84–0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors.

Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.

Establishment and validation of five autophagy-related signatures for predicting survival and immune microenvironment in glioma

BACKGROUND

Gliomas, especially Glioblastoma multiforme, are the most frequent type of primary tumors in central nervous system. Increasing researches have revealed the relationship between autophagy and tumor, while the molecular mechanism of autophagy in glioma is still rarely reported.

OBJECTIVE

Our research aims to conform the autophagy-related genes (ARGs) implicated in the development and progression of glioma and improve our understanding of autophagy in glioma.

METHODS

20 candidate ARGs were screened through the protein-protein interaction network. We also downloaded the publicly accessible glioma data for 665 individuals from TCGA and 970 individuals from CGGA with RNA sequences and clinicopathological information. Subsequently, univariate and multivariate Cox regression analysis identified 5 key ARGs among the 20 candidate genes as key prognostic genes for survival, GSEA and immune response analysis.

RESULTS

ATG5, BCL2L1, CASP3, CASP8, GAPDH were identified as key ARGs in our research. Further studies showed that the high-risk population was linked to a dismal prognosis and suggested an immune-inhibitory microenvironment. GSEA results demonstrated that high risk population was closely related to DNA repair, hypoxia pathways, implicated in immunosuppression and carcinogenesis. Through CMap, we finally identified 14 candidate drugs for the ARG high risk population.

CONCLUSIONS

This study established and verified an ARG risk model, which can serve as an independent predictor for prognosis, reflect on the strength of the immune response and predict the potential drugs in glioma. Our findings offer new understandings of ARG molecular mechanism and promising therapeutic targets for glioma treatment.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Promoting Prognostic Model Application: A Review Based on Gliomas

Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review

BACKGROUND

Gliomas are the most common and aggressive tumors of the central nervous system. A robust and widely used blood-based biomarker for glioma has not yet been identified. In recent years, a plethora of new research on blood-based biomarkers for glial tumors has been published. In this review, we question which molecules, including proteins, nucleic acids, circulating cells, and metabolomics, are most promising blood-based biomarkers for glioma diagnosis, prognosis, monitoring and other purposes, and align them to the seminal processes of cancer.

METHODS

The Pubmed and Embase databases were systematically searched. Biomarkers were categorized in the identified biomolecules and biosources. Biomarker characteristics were assessed using the area under the curve (AUC), accuracy, sensitivity and/or specificity values and the degree of statistical significance among the assessed clinical groups was reported.

RESULTS

7,919 references were identified: 3,596 in PubMed and 4,323 in Embase. Following screening of titles, abstracts and availability of full-text, 262 articles were included in the final systematic review. Panels of multiple biomarkers together consistently reached AUCs >0.8 and accuracies >80% for various purposes but especially for diagnostics. The accuracy of single biomarkers, consisting of only one measurement, was far more variable, but single microRNAs and proteins are generally more promising as compared to other biomarker types.

CONCLUSION

Panels of microRNAs and proteins are most promising biomarkers, while single biomarkers such as GFAP, IL-10 and individual miRNAs also hold promise. It is possible that panels are more accurate once these are involved in different, complementary cancer-related molecular pathways, because not all pathways may be dysregulated in cancer patients. As biomarkers seem to be increasingly dysregulated in patients with short survival, higher tumor grades and more pathological tumor types, it can be hypothesized that more pathways are dysregulated as the degree of malignancy of the glial tumor increases. Despite, none of the biomarkers found in the literature search seem to be currently ready for clinical implementation, and most of the studies report only preliminary application of the identified biomarkers. Hence, large-scale validation of currently identified and potential novel biomarkers to show clinical utility is warranted.

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

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The Immune-related gene pairs (IRGPs) pronostic signature for LGG is correlated with immune cells infiltration.

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WGCNA presented a gene set correlating with immune cells infiltration and genes co-expression relationships were visualized.

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The nomogram constrcted by three IRGPs and clinical factors is a novel tailored tool for individual-level prediction.

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.

Analysis of the EGFR Amplification and CDKN2A Deletion Regulated Transcriptomic Signatures Reveals the Prognostic Significance of SPATS2L in Patients With Glioma

Purpose: This study was conducted in order to analyze the prognostic effects of epidermal growth factor receptor (EGFR) and CDKN2A alterations and determine the prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioblastoma (GBM) or lower grade glioma (LGG).

Methods: The alteration frequencies of EGFR and CDKN2A across 32 tumor types were derived from cBioPortal based on The Cancer Genome Atlas (TCGA) datasets. The Kaplan–Meier analysis was used to determine the prognostic significance of EGFR and CDKN2A alterations. EGFR and CDKN2A alterations on regulated expression signatures were identified from RNA-seq data in the TCGA GBM datasets. The prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioma was determined using the TCGA and the Chinese Glioma Genome Atlas (CGGA) datasets.

Results: Compared with the other 31 tumor types, EGFR amplification and CDKN2A deletion particularly occurred in patients with GBM. GBM patients with EGFR amplification or CDKN2A deletion demonstrated poor prognosis. Statistical analysis showed the coexistence of EGFR alteration and CDKN2A deletion in GBM patients. We identified 864 genes which were commonly regulated by EGFR amplification and CDKN2A deletion, and those genes were highly expressed in brain tissues and associated with the cell cycle, EBRR2, and MAPK signaling pathways. Spermatogenesis-associated serine-rich 2-like gene (SPATS2L) was upregulated in GBM patients with EGFR amplification or CDKN2A alteration. Higher expression levels of SPATS2L were associated with worse prognosis in patients with GBM in both TCGA and CGGA datasets. Moreover, the expression levels of SPATS2L were higher in patients with a mesenchymal subtype of GBM. Statistical analysis also showed that the coexistence of EGFR alteration and CDKN2A deletion was significant in patients with LGG. SPATS2L was upregulated in LGG patients with EGFR amplification or CDKN2A alteration. Furthermore, higher expression levels of SPATS2L were associated with worse prognosis in patients with LGG in both TCGA and CGGA datasets. The expression levels of SPATS2L were higher in patients with an astrocytoma subtype of LGG. Finally, the coexistence and unfavorable prognostic effects of EGFR amplification and CDKN2A alteration were validated using the Memorial Sloan Kettering Cancer Center (MSKCC) glioma datasets.

Conclusions: EGFR amplification and CDKN2A deletion of the regulated gene SPATS2L have significant prognostic effects in patients with GBM or LGG.

Construction of an Immune-Associated Gene-Based Signature in Muscle-Invasive Bladder Cancer

Background

In recent years, immune-associated genes (IAGs) have been documented as having critical roles in the occurrence and progression of muscle-invasive bladder cancer (MIBC). Novel immune-related biomarkers and a robust prognostic signature for MIBC patients are still limited. The study is aimed at developing an IAG-based signature to predict the prognosis of MIBC patients.

Methods

In the present study, we identified differentially expressed IAGs in MIBC by using transcriptomics data from The Cancer Genome Atlas (TCGA) database and proteomics data from our samples. We further constructed an IAG-based signature and evaluated its prognostic and predictive value by survival analysis and nomogram. Tumor Immune Estimation Resource (TIMER) was applied to explore the correlation between the IAG-based signature and immune cell infiltration in the microenvironment of MIBC.

Results

A total of 22 differentially expressed IAGs were identified, and 2 IAGs (NR2F6 and AHNAK) were used to establish a prognostic signature. Subsequently, survival analysis showed that high-risk scores were significantly correlated with poor overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) of MIBC patients. A prognostic nomogram was constructed by integrating clinical factors with the IAG-based signature risk score. In addition, the IAG-based signature risk score was positively associated with the infiltration of macrophages and dendritic cells in MIBC.

Conclusions

We constructed and verified a novel IAG-based signature, which could predict the prognosis of MIBC and might reflect the status of the immune microenvironment of MIBC. Further studies in more independent clinical cohorts and further experimental exploration of the prognostic IAG-based signature are still needed.

An Immune-Related Signature for Predicting the Prognosis of Lower-Grade Gliomas

Background

Lower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma.

Methods

LGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed.

Results

Sixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060–8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME.

Conclusion

The immune-related prognostic signature and the prognostic nomogram could accurately predict survival.

Three-Dimensional Nuclear Telomere Profiling as a Biomarker for Recurrence in Oligodendrogliomas: A Pilot Study

Mechanisms of recurrence in oligodendrogliomas are poorly understood. Recurrence might be driven by telomere dysfunction-mediated genomic instability. In a pilot study, we investigated ten patients with oligodendrogliomas at the time of diagnosis (first surgery) and after recurrence (second surgery) using three-dimensional nuclear telomere analysis performed with quantitative software TeloView^® (Telo Genomics Corp, Toronto, Ontario, Canada). 1p/19q deletion status of each patient was determined by fluorescent in situ hybridization on touch preparation slides. We found that a very specific 3D telomeric profile was associated with two pathways of recurrence in oligodendrogliomas independent of their 1p/19q status: a first group of 8 patients displayed significantly different 3D telomere profiles between both surgeries (p < 0.0001). Their recurrence happened at a mean of 231.375 ± 117.42 days and a median time to progression (TTP) of 239 days, a period defined as short-term recurrence; and a second group of three patients displayed identical 3D telomere profiles between both surgery samples (p > 0.05). Their recurrence happened at a mean of 960.666 ± 86.19 days and a median TTP of 930 days, a period defined as long-term recurrence. Our results suggest a potential link between nuclear telomere architecture and telomere dysfunction with time to recurrence in oligodendrogliomas, independently of the 1p/19q status.

An Autophagy-Related Gene Signature Associated With Clinical Prognosis and Immune Microenvironment in Gliomas

Glioma is one of the leading causes of death from cancer, and autophagy-related genes (ARGs) play an important role in glioma occurrence, progression, and treatment. In this study, the gene expression profiles and clinical data of glioma patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), respectively. ARGs were obtained from the Human Autophagy Database. We analyzed the expression of the ARGs in glioma and found that 73 ARGs were differentially expressed in tumor and normal tissues. Univariate Cox regression analysis was used to identify prognostic differentially expressed ARGs (PDEARGs). Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on the PDEARGs to determine the risk genes; and BRIC5, NFE2L2, GABARAP, IKBKE, BID, MAPK3, FKBP1B, MAPK8IP1, PRKCQ, CX3CL1, NPC1, HSP90AB1, DAPK2, SUPT20H, and PTEN were selected to establish a prognostic risk score model for TCGA and CGGA cohorts. This model accurately stratified patients with different survival outcomes, and the autophagy-related signature was also appraised as being an independent prognostic factor. We also constructed a prognostic nomogram using risk score, age, gender, WHO grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q co-deletion status; and the calibration plots showed excellent prognostic performance. Finally, Pearson correlation analysis suggested that the ARG signature also played an essential role in the tumor immune microenvironment. In summary, we constructed and verified a novel autophagy-related signature that was tightly associated with the tumor immune microenvironment and could serve as an independent prognostic biomarker in gliomas.

Integrated analysis of serum lipid profile for predicting clinical outcomes of patients with malignant biliary tumor

Background

Serum lipids were reported to be the prognostic factors of various cancers, but their prognostic value in malignant biliary tumor (MBT) patients remains unclear. Thus we aim to assess and compare prognosis values of different serum lipids, and construct a novel prognostic nomogram based on serum lipids.

Methods

Patients with a confirmed diagnosis of MBT at our institute from 2003 to 2017 were retrospectively reviewed. Prognosis-related factors were identified via univariate and multivariate Cox regression analyses. Then the novel prognostic nomogram and a 3-tier staging system were constructed based on these factors and further compared to the TNM staging system.

Results

A total of 368 patients were included in this study. Seven optimal survival-related factors—TC/HDL >  10.08, apolipoprotein B >  0.9 g/L, lipoprotein> 72 mg/L, lymph node metastasis, radical cure, CA199 > 37 U/mL, and tumor differentiation —were included to construct the prognostic nomogram. The C-indexes in training and validation sets were 0.738 and 0.721, respectively. Besides, ROC curves, calibration plots, and decision curve analysis all suggested favorable discrimination and predictive ability. The nomogram also performed better predictive ability than the TNM system and nomogram without lipid parameters. And the staging system based on nomogram also presented better discriminative ability than TNM system (P < 0.001).

Conclusions

The promising prognostic nomogram based on lipid parameters provided an intuitive method for performing survival prediction and facilitating individualized treatment and was a great complement to the TNM staging system in predicting overall survival.

A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer

Current studies have shown that long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers in multiple cancers. Therefore, we postulated that expression patterns of multiple lncRNAs combined into a single signature could improve clinicopathological risk stratification and prediction of overall survival rate for breast cancer patients. Two algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to select candidate lncRNAs. Univariate and multivariate Cox regression analyses were employed to construct a seven-lncRNA signature for breast cancer. Stratified analysis revealed that the signature was significantly associated with multiple clinicopathological risk factors. For clinical use, we developed a nomogram model to predict overall survival and odds of death for breast cancer patients. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithm and ESTIMATE method were employed to assess the relative immune cell infiltrations of each sample. Differentially infiltration of immune cells and diverse tumour mutation burden (TMB) scores might give rise to the efficacy of lncRNA signature for predicting the overall survival of patients. Correlation analysis implied that LINC01215 was associated with multiple immune-related signalling pathways. A seven-lncRNA prognostic signature is a reliable tool to predict the prognosis of breast cancer patients.

Development and Validation of a Novel DNA Methylation-Driven Gene Based Molecular Classification and Predictive Model for Overall Survival and Immunotherapy Response in Patients With Glioblastoma: A Multiomic Analysis

Purpose

Glioblastoma (GBM) is the most common primary malignant tumor of the central nervous system, with a 5-year overall survival (OS) rate of only 5.6%. This study aimed to develop a novel DNA methylation-driven gene (MDG)-based molecular classification and risk model for individualized prognosis prediction for GBM patients.

Methods

The DNA methylation profiles (458 samples) and gene expression profiles (376 samples) of patients were enrolled to identify MDGs using the MethylMix algorithm. Unsupervised consensus clustering was performed to develop the MDG-based molecular classification. By performing the univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis, a MDG-based prognostic model was developed and validated. Then, Bisulfite Amplicon Sequencing (BSAS) and quantitative real-time polymerase chain reaction (qPCR) were performed to verify the methylation and expressions of MDGs in GBM cell lines.

Results

A total of 199 MDGs were identified, the expression patterns of which enabled TCGA and CGGA GBM patients to be divided into 2 clusters by unsupervised consensus clustering. Cluster 1 patients commonly exhibited a poor prognosis, were older in age, and were more sensitive to immunotherapies. Then, six MDGs (ANKRD10, BMP2, LOXL1, RPL39L, TMEM52, and VILL) were further selected to construct the prognostic risk score model, which was validated in the CGGA cohort. Kaplan-Meier survival analysis demonstrated that high-risk patients had significantly poorer OS than low-risk patients (logrank P = 3.338 × 10-6). Then, a prognostic nomogram was constructed and validated. Calibration plots, receiver operating characteristic curves, and decision curve analysis indicated excellent predictive performance for the nomogram in both the TCGA training and CGGA validation cohorts. Finally, in vitro BSAS and qPCR analysis validated that the expressions of the MDGs were negatively regulated by methylations of target genes, especially promoter region methylation.

Conclusion

The MDG-based prognostic model could serve as a promising prognostic indicator and potential therapeutic target to facilitate individualized survival prediction and better treatment options for GBM patients.

Development of a Nomogram With Alternative Splicing Signatures for Predicting the Prognosis of Glioblastoma: A Study Based on Large-Scale Sequencing Data

Purpose: Alternative splicing (AS) was reported to play a vital role in development and progression of glioblastoma (GBM), the most common and fatal brain tumor. Systematic analysis of survival-associated AS event profiles and prognostic prediction model based on multiple AS events in GBM was needed. Methods: Genome-wide AS and RNA sequencing profiles were generated in 152 patients with GBM in the cancer genome atlas (TCGA). Prognosis-associated AS events were screened by integrated Cox regression analysis to construct the prognostic risk score model in the training cohort (n = 101). The AS-based signature and clinicopathologic parameters were applied to construct a prognostic nomogram for 0.5-, 1-, and 3-year OS prediction. Finally, the regulatory networks between prognostic AS events and splicing factors (SFs) were constructed. Results: A total of 1,598 prognosis-related AS events from 1,183 source genes were determined. Eight prognostic risk score model based on integrated AS events and 7 AS types were established, respectively. Concordance index (C-index) and receiver operating characteristic (ROC) curve analysis demonstrated powerful ability in distinguishing patients' outcomes. Only Alternate Donor site (AD) and Exon Skip (ES) signature out of the eight types of AS signature were identified as independent prognostic factors for GBM, which was validated in the internal validation cohort. The nomogram with age, new event, pharmaceutical therapy, radiation therapy, AD signature and ES signature were constructed, with C-index of 0.892 (95% CI, 0.853-0.931; P = 5.13 × 10^-15). Calibration plots, ROC, and decision curve analysis suggested excellent predictive performance for the nomogram in both TCGA training cohort and validation cohort. Splicing network indicated distinguished correlations between prognostic AS events and SFs in GBM patients. Conclusions: AS-based prediction model could serve as a promising prognostic predictor and potential therapeutic target for GBM, facilitating better treatment strategies in clinical practice.

Prognostic model and nomogram construction based on autophagy signatures in lower grade glioma

The median survival time of lower grade glioma (LGG) tumors spans a wide range of 2-10 years and is highly dependent on the molecular characteristics and tumor location. Currently, there is no prognostic predictor for these tumors based on autophagy-related (ATG) genes. A prognostic risk score model based on the most significant seven ATG genes was established for LGG. These seven genes, including GRID2, FOXO1, MYC, PTK6, IKBKE, BIRC5, and TP73, have been screened as potentially therapeutic targets. The Kaplan-Meier survival curve analyses validated that patients with high or low risk scores had significantly different overall survival. Following the multivariate Cox regression and area under the ROC curve (AUC) analysis, a final prognostic model based on age, World Health Organization grade, 1p19q-codeletion status, and ATG risk score was performed as an independent prognostic indicator (training set: p = 4.09E-05, AUC = 0.901; validation set-1: p = .00069, AUC = 0.808; validation set-2: p = .0376, AUC = 0.830). Subsequently, a prognostic nomogram was constructed for individualized survival prediction. The calibration plots showed excellent predict efficiency between probability and actual overall survival. In this study, we provided several potential biomarkers for further developing potentially therapeutic targets of LGG. We also established a prognostic model and nomogram to improve the clinical glioma management and assist individualized survival prediction.

Comprehensive Transcriptomic Analysis and Experimental Validation Identify lncRNA HOXA-AS2/miR-184/COL6A2 as the Critical ceRNA Regulation Involved in Low-Grade Glioma Recurrence

Purpose

The recurrence and metastasis of glioma are closely related to complex regulatory networks among protein-coding genes, lncRNAs and microRNAs. The aim of this study was to investigate core genes, lncRNAs, miRNAs and critical ceRNA regulatory mechanisms, which are involved in lower-grade glioma (LGG) recurrence.

Materials and Methods

We employed multiple datasets from Chinese Glioma Genome Atlas (CGGA) database and The Cancer Genome Atlas (TCGA) to perform comprehensive transcriptomic analysis. Further in vitro experiments including cell proliferation assay, luciferase reporter assay, and Western blot were performed to validate our results.

Results

Recurrent LGG and glioblastoma (GBM) showed different transcriptome characteristics with less overlap of differentially expressed protein-coding genes (DEPs), lncRNAs (DELs) and miRNAs (DEMs) compared with primary samples. There were no overlapping gene in ontology (GO) terms related to GBM recurrence in the TCGA and CGGA databases, but there were overlaps associated with LGG recurrence. GO analysis and protein–protein interaction (PPI) network analysis identified three core genes: TIMP1, COL1A1 and COL6A2. By hierarchical cluster analysis of them, LGGs could be clustered as Low_risk and High_risk group. The High_risk group with high expression of TIMP1, COL1A1, and COL6A2 showed worse prognosis. By coexpression networks analysis, competing endogenous RNA (ceRNA) network analysis, cell proliferation assay and luciferase reporter assay, we confirmed that lncRNA HOXA-AS2 functioned as a ceRNA for miR-184 to regulate expression of COL6A2, which induced cell proliferation of low-grade glioma.

Conclusion

In this study, we revealed a 3-hub protein-coding gene signature to improve prognostic prediction in LGG, and identified a critical ceRNA regulation involved in LGG recurrence.

Prognostic and predictive value of an immune infiltration signature in diffuse lower-grade gliomas

BACKGROUNDLower-grade gliomas (LGGs) vary widely in terms of the patient's overall survival (OS). There is no current, valid method that could exactly predict the survival. The effects of intratumoral immune infiltration on clinical outcome have been widely reported. Thus, we aim to develop an immune infiltration signature to predict the survival of LGG patients.METHODSWe analyzed 1216 LGGs from 5 public data sets, including 2 RNA sequencing data sets and 3 microarray data sets. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select an immune infiltration signature and build a risk score. The performance of the risk score was assessed in the training set (329 patients), internal validation set (140 patients), and 4 external validation sets (405, 118, 88, and 136 patients).RESULTSAn immune infiltration signature consisting of 20 immune metagenes was used to generate a risk score. The performance of the risk score was thoroughly verified in the training and validation sets. Additionally, we found that the risk score was positively correlated with the expression levels of TGF-β and PD-L1, which were important targets of combination immunotherapy. Furthermore, a nomogram incorporating the risk score, patient's age, and tumor grade was developed to predict the OS, and it performed well in all the training and validation sets (C-index: 0.873, 0.881, 0.781, 0.765, 0.721, and 0.753).CONCLUSIONThe risk score based on the immune infiltration signature has reliable prognostic and predictive value for patients with LGGs and is a potential biomarker for the cotargeting immunotherapy.FUNDINGThis work was supported by The National Natural Science Foundation of China (grant nos. 81472370 and 81672506), the Natural Science Foundation of Beijing (grant no. J180005), the National High Technology Research and Development Program of China (863 Program, grant no. 2014AA020610), and the National Basic Research Program of China (973 Program, grant no. 2014CB542006).

Development and validation of a prognostic immune-associated gene signature in clear cell renal cell carcinoma

BACKGROUND

Recent studies have demonstrated that immune-associated genes (IAGs) play an important role in the occurrence and progression of clear renal clear cell carcinoma (ccRCC). Novel biomarkers and a reliable prognostic prediction model for ccRCC patients are still limited. The objective of this study was to develop a IAGs signature and validate its prognostic value in ccRCC using bioinformatic methods and publicly database.

METHODS

In the present study, we identified differentially expressed IAGs in ccRCC based on The Cancer Genome Atlas (TCGA) database. A prognostic IAGs risk model was further developed and its prognostic and predictive value was evaluated by survival analysis and nomogram.

RESULTS

A total of 681 differentially expressed IAGs were identified and seven IAGs (IFI30, WNT5A, IRF9, AGER, PLAUR, TEK, BID) were finally selected in a IAGs signature. Survival analysis revealed that high IAGs risk scores were significantly related to poor survival outcomes. The IAGs signature was demonstrated as an independent prognostic factor and closely related to the metastasis status of ccRCC. A nomogram with clinicopathologic characteristics and IAGs signature was also constructed to superiorly predict prognosis of ccRCC patients.

CONCLUSIONS

We identified seven IAGs as a potential signature for reflecting the prognosis of ccRCC based on TCGA database. Further clinical trials are needed to validate our observations and the mechanisms underlying the prognostic value of IAGs signature in ccRCC also deserve further experimental exploration.

Prognostic factors of patients with Gliomas - an analysis on 335 patients with Glioblastoma and other forms of Gliomas

Background

The prognosis of glioma is poor, despite recent advances in diagnosis and treatment of the disease. It is important to investigate the clinical characteristics and prognostic factors of glioma so as to provide basis for treatment and management of patients.

Method

A total of 335 patients with glioma were included in this study. These patients were admitted to the medical center between November 2015 and December 2018. The clinical data, including demographic data, tumor characteristics, treatment strategy, expression pattern of tumor markers, and survival data, were retrospectively reviewed. Survival data were analyzed using Kaplan-Meier curves with log-rank test, while multivariate analysis Cox regression model was used to investigate risk factors for mortality.

Results

In this patient cohort, glioblastoma (40%), diffuse glioma (14.6%) and oligodendroglioma (9.6%) were the most common pathological types. The expression of Ki-67 was associated with several clinicopathological parameters (e.g. tumor type, grade, and number of lesions). In addition, Ki-67 correlated with the mortality within the first year of the post-treatment follow-up (P <  0.001). Kaplan-Maier analysis revealed that older patients (≥ 45 years) displayed worse prognosis than those aged under 45 years (P = 0.038). Dismal prognosis was also associated with clinical parameters, including high tumor grade, multiple lesions, and Karnofsky performance score (KPS). Multivariate analysis showed that low KPS (< 85) increased the risk of mortality by 2.3 folds with a 95% CI of 1.141 to 4.776 (P = 0.020). Low tumor grade (grade 1–2) oppositely reduced the mortality risk by 0.22 folds (95% CI, 0.065 to 0.763, P = 0.0168).

Conclusion

KPS and tumor grade were independent prognostic factors in patients with gliomas.

Identification and validation of a 21-mRNA prognostic signature in diffuse lower-grade gliomas

PURPOSE

Diffuse low-grade and intermediate-grade gliomas, also known as lower-grade gliomas (LGGs), are a class of central nervous system tumors. Overall survival varies greatly between patients, highlighting the importance of evaluating exact outcomes to facilitate individualized clinical management. We aimed to identify an mRNA-based prognostic signature to predict the survival of patients with LGGs.

METHODS

A total of 874 LGGs from two public datasets were included. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the most prognostic mRNAs and build a risk score. A nomogram incorporating the risk score and clinical factors was established for individualized survival prediction. The performance of the nomogram was assessed in the training set (329 patients), internal validation set (140 patients), and external validation set (405 patients).

RESULTS

21 most prognostic mRNAs remained following the LASSO Cox regression. The 21-mRNA signature successfully stratified patients into high- and low-risk groups (P < 0.001 for all datasets in Kaplan-Meier analysis). Subsequent gene set enrichment analysis identified 19 essential biological processes in high-risk LGGs. Furthermore, a nomogram incorporating the risk score, age, grade, and 1p/19q status was developed with favorable calibration and high predictive accuracy in the training set and validation sets (C-index: 0.877, 0.878, and 0.812, respectively).

CONCLUSION

The 21-mRNA signature has reliable prognostic value for LGGs and might facilitate the effective stratification and individualized management of patients.

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Glioblastoma (GBM) is the most common brain tumor with significant morbidity and mortality. Autophagy plays a vital role in GBM development and progression. We aimed to establish an autophagy-related multigene expression signature for individualized prognosis prediction in patients with GBM. Differentially expressed autophagy-related genes (DE-ATGs) in GBM and normal samples were screened using TCGA. Univariate and multivariate Cox regression analyses were performed on DE-ATGs to identify the optimal prognosis-related genes. Consequently, NRG1 (HR=1.142, P=0.008), ITGA3 (HR=1.149, P=0.043), and MAP1LC3A (HR=1.308, P=0.014) were selected to establish the prognostic risk score model and validated in the CGGA validation cohort. GSEA revealed that these genes were mainly enriched in cancer- and autophagy-related KEGG pathways. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P= 6.955×10^-5). The autophagy signature was identified as an independent prognostic factor. Finally, a prognostic nomogram including the autophagy signature, age, pharmacotherapy, radiotherapy, and IDH mutation status was constructed, and TCGA/CGGA-based calibration plots indicated its excellent predictive performance. The autophagy-related three-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for GBM. The prognostic nomogram could assist individualized survival prediction and improve treatment strategies.

Identification of a prognostic 28-gene expression signature for gastric cancer with lymphatic metastasis

Gastric cancer (GC) patients have high mortality due to late-stage diagnosis, which is closely associated with lymph node metastasis. Exploring the molecular mechanisms of lymphatic metastasis may inform the research into early diagnostics of GC. In the present study, we obtained RNA-Seq data from The Cancer Genome Altas and used Limma package to identify differentially expressed genes (DEGs) between lymphatic metastases and non-lymphatic metastases in GC tissues. Then, we used an elastic net-regularized COX proportional hazard model for gene selection from the DEGs and constructed a regression model composed of 28-gene signatures. Furthermore, we assessed the prognostic performance of the 28-gene signature by analyzing the receive operating characteristic curves. In addition, we selected the gene PELI2 amongst 28 genes and assessed the roles of this gene in GC cells. The good prognostic performance of the 28-gene signature was confirmed in the testing set, which was also validated by GSE66229 dataset. In addition, the biological experiments showed that PELI2 could promote the growth and metastasis of GC cells by regulating vascular endothelial growth factor C. Our study indicates that the identified 28-gene signature could be considered as a sensitive predictive tool for lymphatic metastasis in GC.

Identification of biomarkers in macrophages of atherosclerosis by microarray analysis

BACKGROUND

Atherosclerotic cardiovascular disease (ASCVD) refers to a series of diseases caused by atherosclerosis (AS). It is one of the most important causes of death worldwide. According to the inflammatory response theory, macrophages play a critical role in AS. However, the potential targets associated with macrophages in the development of AS are still obscure. This study aimed to use bioinformatics tools for screening and identifying molecular targets in AS macrophages.

METHODS

Two expression profiling datasets (GSE7074 and GSE9874) were obtained from the Gene Expression Omnibus dataset, and differentially expressed genes (DEGs) between non-AS macrophages and AS macrophages were identified. Functional annotation of the DEGs was performed by analyzing the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. STRING and Cytoscape were employed for constructing a protein-protein interaction network and analyzing hub genes.

RESULTS

A total of 98 DEGs were distinguished between non-AS macrophages and AS macrophages. The functional variations in DEGs were mainly enriched in response to hypoxia, respiratory gaseous exchange, protein binding, and intracellular, ciliary tip, early endosome membrane, and Lys63-specific deubiquitinase activities. Three genes were identified as hub genes, including KDELR3, CD55, and DYNC2H1.

CONCLUSION

Hub genes and DEGs identified by using microarray techniques can be used as diagnostic and therapeutic biomarkers for AS.

The Germline Variants rs61757955 and rs34988193 Are Predictive of Survival in Lower Grade Glioma Patients

Lower grade gliomas are invasive brain tumors that are difficult to completely resect neurosurgically. They often recur following resection and progress, resulting in death. Although previous studies have shown that specific germline variants increase the risk of tumor formation, no previous study has screened many germline variants to identify variants predictive of survival in patients with glioma. In this study, we present an approach to identify the small fraction of prognostic germline variants from the pool of over four million variants that we variant called in The Cancer Genome Atlas whole-exome sequencing and RNA sequencing datasets. We identified two germline variants that are predictive of poor patient outcomes by Cox regression, controlling for eleven covariates. rs61757955 is a germline variant found in the 3' UTR of GRB2 associated with increased KRAS signaling, CIC mutations, and 1p/19q codeletion. rs34988193 is a germline variant found in the tumor suppressor gene ANKDD1a that causes an amino acid change from lysine to glutamate. This variant was found to be predictive of poor prognosis in two independent lower grade glioma datasets and is predicted to be within the top 0.06% of deleterious mutations across the human genome. The wild-type residue is conserved in all 22 other species with a homologous protein. IMPLICATIONS: This is the first study presenting an approach to screening many germline variants to identify variants predictive of survival and our application of this methodology revealed the germline variants rs61757955 and rs34988193 as being predictive of survival in patients with lower grade glioma.

Isolation, structure elucidation, and synthesis of (±)-millpuline A with a suppressive effect in miR-144 expression

A pair of natural biflavone enantiomers stereo-selectively inhibit pre-miR-144 dicing to modulate the miR-144-3p/Nrf2 pathway.

Evaluating the Oncogenic and Tumor Suppressor Role of XPO5 in Different Tissue Tumor Types

The miRNAs nuclear export protein XPO5 has been previously studied in several individual malignancies. In our recent study we have demonstrated that excess levels of XPO5 enhanced the proliferation of prostate cancer cells. Similarly, there are studies to support the inhibitory role of XPO5 in cancers. In order to evaluate discrepancies in the expression levels of XPO5 in differential tumor types, we quantified the expression of XPO5 using gene expression RNA-seq data for several tumor types which were independently confirmed by immunohistochemistry in multiple organs cancer tissue microarray (TMAs) experiment. We found that while some tumors (Breast, Bladder, Lymph-node, Lung, Esophagus and Ovary) showed higher differences between normal and malignant tumors in XPO5 expression, there were tissues (Kidney and Brain) that have a significantly lower XPO5 expression in malignant tumors. We further studies these observations of overexpression and down-regulation of XPO5 in breast and kidney cancer cell lines and found that XPO5 might have a dual role in promoting or inhibiting tumor growth in different cancer tissue types.