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

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.     

Prognostic implication of histological oligodendroglial tumor component: clinicopathological analysis of 111 cases of malignant gliomas

The favorable prognosis of high-grade oligodendroglial tumor such as glioblastoma (GBM) with oligodendroglioma component (GBMO) has been suggested; however, the studies which examine the prognostic significance of oligodendroglial tumor were limited. In this study, we performed a histopathology-based reevaluation of 111 cases of high grade gliomas according to the latest World Health Organization (WHO), and compared the clinical outcomes between oligodendroglial tumors and pure astrocytic tumors. The survival analysis revealed that the patients with high grade oligodendroglial tumor including GBMO significantly indicated better prognosis compared to the patients with high grade pure astrocytic tumors (GBM and AA, anaplastic astrocytoma) as expected, and the obtained survival curves were almost identical to those from the patients with conventional Grade III or Grade IV tumors, respectively. Moreover, if the cases of oligodendroglial tumor were histopathologically excluded, the patients with AA exhibited extremely poor prognosis which was similar to that of GBM, suggesting that the histological identification of oligodendroglial tumor component, even partially, prescribe the prognosis of high grade glioma patients. This is the prominent report of retrospective clinicopathological analysis for high-grade gliomas throughout Grade III and IV, especially referring to the prognostic value of histological oligodendroglial tumor component; in addition, our results might offer an alternative aspect for the grading of high-grade astrocytic/oligodendroglial tumors.     

Exploring Radiologic Criteria for Glioma Grade Classification on the BraTS Dataset

ObjectivesGlioma grading using maching learning on magnetic resonance data is a growing topic. According to the World Health Organization (WHO), the classification of glioma discriminates between low grade gliomas (LGG), grades I, II; and high grade gliomas (HGG), grades III, IV, leading to major issues in oncology for therapeutic management of patients. A well-known dataset for machine-based grade prediction is the MICCAI Brain Tumor Segmentation (BraTS) dataset. However this dataset is not divided into WHO-defined LGG and HGG, since it combines grades I, II and III as “lower grades gliomas”, while its HGG category only presents grade IV glioblastoma multiform. In this paper we want to train a binary grade classifier and investigate the consistency of the original BraTS labels with radiologic criteria using machine-aided predictions.Material and methodsUsing WHO-based radiomic features, we trained a SVM classifier on the BraTS dataset, and used the prediction score histogram to investigate the behaviour of our classifier on the lower grade population. We also asked 5 expert radiologists to annotate BraTS images between low (as opposed to lower) grade and high grade glioma classes, resulting in a new groundtruth.ResultsOur first training reached 84.1% accuracy. The prediction score histogram allows us to identify the radiologically high grade patients among the original lower grade population of the BraTS dataset. Training another SVM on our new radiologically WHO-aligned groundtruth shows robust performances despite important class imbalance, reaching 82.4% accuracy.ConclusionOur results highlight the coherence of radiologic criteria for low grade versus high grade classification under WHO terms. We also show how the histogram of prediction scores and crossed prediction scores can be used as tools for data exploration and performance evaluation. Therefore, we propose to use our radiological groundtruth for future development on binary glioma grading.     

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.     

A 16-mRNA signature optimizes recurrence-free survival prediction of Stages II and III gastric cancer

High-throughput messenger RNA (mRNA) analysis has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. Here, we constructed a signature to predict the recurrence risk of Stages II and III gastric cancer (GC) patients. A least absolute shrinkage and selection operator method Cox regression model was utilized to construct the signature. Using this method, a 16-mRNA signature was identified to be associated with the relapse-free survival of Stages II and III GCs in training dataset GSE62254 (n = 194). Then this signature was validated in an independent Gene Expression Omnibus cohort GSE26253 (n = 297) and a dataset of The Cancer Genome Atlas (TCGA; n = 235). This classifier could successfully screen out the high-risk Stages II and III GCs in the training cohort (hazard ratio [HR] = 40.91; 95% confidence interval [CI] = 5.58–299.7; p < .0001). Analysis in two independent validation cohorts yielded consistent results (GSE26253: HR = 1.69, 95% CI = 1.17–2.43,; p = .0045; TCGA: HR = 2.01, 95% CI = 1.13–3.56, p = .0146). Cox regression analyses revealed that the risk score derived from this signature was an independent risk factor in Stages II and III GCs. Besides, a nomogram was constructed to serve clinical practice. Through gene set variation analysis, we found several gene sets associated with chemotherapeutic drug resistance and tumor metastasis significantly enriched in high-risk patients. In summary, this 16-mRNA signature can be used as a powerful tool for prognostic evaluation and help clinicians identify high-risk patients.     

Molecular Biological Determinations of Meningioma Progression and Recurrence

Meningiomas are tumors that arise from the coverings of the brain or spinal cord. 5% of the cases turn into malignant forms with aggressive clinical behavior and increased risk of tumor recurrence. One hundred and five patients with meningiomas were operated by open surgery. To investigate predictors of meningioma recurrence in total 124 samples of 105 patients were investigated by iFISH. Dual-probe hybridization was performed to access chromosomal alterations of chromosomes 1p-, 9p- and 22q. Additionally, methylation of TIMP3 and p16 was analyzed with MS-PCR. Of the 105 investigated tumors 59.1% (62/105) were WHO grade I, 33.3% (35/105) were WHO grade II and 7.7% (8/105) were anaplastic meningiomas (grade III), respectively. The histopathological data correlates with the recurrence rate of the investigated meningiomas. Hypermethylation of TIMP3 was detected in 13.3% of all meningiomas: 10.9% in WHO grade I meningiomas, 25.0% in grade II and 14.3% in grade III meningiomas, respectively. No correlation of TIMP3 hypermethylation with tumor recurrence or WHO grade (p = 0.2) was observed. Interestingly, deletion of 1p36 emerged as a significant predictor of shorter overall survival (log rank test, p<0.001), whereas TIMP3 promoter methylation had no significant effect on overall survival (log rank test, p = 0.799). The results of the current study support the finding that the deletion of chromosome 1p is an independent marker of meningioma recurrence and progression (p = 0.0097). Therefore the measurement of genetic aberrations in meningiomas allows in a combined histological approach a more precise assessment of the prognosis of meningiomas than histopathology alone.     

Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction

Background

Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.

Methodology and Principal Findings

A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.

Conclusions

The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.     

Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.     

Identification of miRNA-Mediated Core Gene Module for Glioma Patient Prediction by Integrating High-Throughput miRNA,mRNA Expression and Pathway Structure

The prognosis of glioma patients is usually poor, especially in patients with glioblastoma (World Health Organization (WHO) grade IV). The regulatory functions of microRNA (miRNA) on genes have important implications in glioma cell survival. However, there are not many studies that have investigated glioma survival by integrating miRNAs and genes while also considering pathway structure. In this study, we performed sample-matched miRNA and mRNA expression profilings to systematically analyze glioma patient survival. During this analytical process, we developed pathway-based random walk to identify a glioma core miRNA-gene module, simultaneously considering pathway structure information and multi-level involvement of miRNAs and genes. The core miRNA-gene module we identified was comprised of four apparent sub-modules; all four sub-modules displayed a significant correlation with patient survival in the testing set (P-values≤0.001). Notably, one sub-module that consisted of 6 miRNAs and 26 genes also correlated with survival time in the high-grade subgroup (WHO grade III and IV), P-value = 0.0062. Furthermore, the 26-gene expression signature from this sub-module had robust predictive power in four independent, publicly available glioma datasets. Our findings suggested that the expression signatures, which were identified by integration of miRNA and gene level, were closely associated with overall survival among the glioma patients with various grades.     

Syndecan-1 expression in human glioma is correlated with advanced tumor progression and poor prognosis

Syndecan-1 has been implicated in tumorigenesis and progression of various human malignancies. Recent studies have demonstrated that syndecan-1 may have a different function and biological activity depending on the specific tumor type. Therefore, the aim of this study was to investigate the clinical significance of syndecan-1 in human gliomas. One hundred and sixteen glioma patients (26 World Health Organization (WHO) grade I, 30 WHO grade II, 30 WHO grade III, and 30 WHO grade IV) and 15 normal brain specimens acquired from 15 patients undergoing surgery for epilepsy as control were collected. Immunohistochemistry assay, quantitative real-time PCR and Western blot analysis were carried out to detect the expression of syndecan-1 at gene and protein levels in glioma samples with different WHO grades. Syndecan-1 gene and protein levels were both higher in glioma tissues compared to controls (both P < 0.001). In addition, its expression levels increased with ascending tumor WHO grades according to the results of immunohistochemistry assay, quantitative real-time PCR and Western blot analysis. Moreover, the survival rate of syndecan-1-positive patients was significantly lower than that of syndecan-1-negative patients (P = 0.006). We further confirmed that the increased expression of syndecan-1 was an independent prognostic indicator in glioma by multivariate analysis (P = 0.01). Our data suggest for the first time that the increased expression of syndecan-1 at gene and protein levels is correlated with advanced tumor progression and poor outcome in patients with glioma. Syndecan-1 might serve as a potential prognosis predictor of this dismal tumor.     

The CIN4 Chromosomal Instability qPCR Classifier Defines Tumor Aneuploidy and Stratifies Outcome in Grade 2 Breast Cancer

Purpose

Quantifying chromosomal instability (CIN) has both prognostic and predictive clinical utility in breast cancer. In order to establish a robust and clinically applicable gene expression-based measure of CIN, we assessed the ability of four qPCR quantified genes selected from the 70-gene Chromosomal Instability (CIN70) expression signature to stratify outcome in patients with grade 2 breast cancer.

Methods

AURKA, FOXM1, TOP2A and TPX2 (CIN4), were selected from the CIN70 signature due to their high level of correlation with histological grade and mean CIN70 signature expression in silico. We assessed the ability of CIN4 to stratify outcome in an independent cohort of patients diagnosed between 1999 and 2002. 185 formalin-fixed, paraffin-embedded (FFPE) samples were included in the qPCR measurement of CIN4 expression. In parallel, ploidy status of tumors was assessed by flow cytometry. We investigated whether the categorical CIN4 score derived from the CIN4 signature was correlated with recurrence-free survival (RFS) and ploidy status in this cohort.

Results

We observed a significant association of tumor proliferation, defined by Ki67 and mitotic index (MI), with both CIN4 expression and aneuploidy. The CIN4 score stratified grade 2 carcinomas into good and poor prognostic cohorts (mean RFS: 83.8±4.9 and 69.4±8.2 months, respectively, p = 0.016) and its predictive power was confirmed by multivariate analysis outperforming MI and Ki67 expression.

Conclusions

The first clinically applicable qPCR derived measure of tumor aneuploidy from FFPE tissue, stratifies grade 2 tumors into good and poor prognosis groups.     

Genetic analysis to complement histopathological diagnosis of brain tumors

Gliomas, the most frequent tumors originating in the human nervous system, are divided into various subtypes. Currently, microscopic examination alone is insufficient for classification and grading so that genetic profiles are increasingly being emphasized in recognition of the emerging role of molecular diagnostic approaches to glioma classification. Glioblastomas (WHO grade IV) may develop de novo (primary glioblastomas) or through progression from lower-grade astrocytomas (secondary glioblastomas), while both glioblastomas show similar histological features. In contrast, they do constitute distinct disease entities that evolve through different genetic pathways, and are likely to differ in prognosis and response to therapy. Oligodendrogliomas (WHO grade II) account for 2.7% of brain tumors and 5-18% of all gliomas. Since this tumor is recognized as a particular subtype of glioma that shows remarkable responses to chemotherapy, a correct diagnosis is of prime importance. The difficulty is that histological differentiation of oligodendrogliomas from diffuse astrocytomas is highly subjective in cases without typical morphological features and there is a lack of reliable immunohistochemical markers. While histological distinction of low-grade gliomas from reactive astrocytes is also often difficult, reactive astrocytes usually lack genetic alterations. More biological and molecular approaches to glioma classification thus appear warranted to provide improved means to achieve correct diagnoses.     

Stem Cell-Like Gene Expression in Ovarian Cancer Predicts Type II Subtype and Prognosis

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer.     

A MiRNA Signature for Defining Aggressive Phenotype and Prognosis in Gliomas

Gliomas represent a disparate group of tumours for which there are to date no cure. Thus, there is a recognized need for new diagnostic and therapeutic approaches based on increased understanding of their molecular nature. We performed the comparison of the microRNA (miRNA) profile of 8 WHO grade II gliomas and 24 higher grade tumours (2 WHO grade III and 22 glioblastomas) by using the Affymetrix GeneChip miRNA Array v. 1.0. A relative quantification method (RT-qPCR) with standard curve was used to confirm the 22 miRNA signature resulted by array analysis. The prognostic performances of the confirmed miRNAs were estimated on the Tumor Cancer Genome Atlas (TCGA) datasets. We identified 22 miRNAs distinguishing grade II gliomas from higher grade tumours. RT-qPCR confirmed the differential expression in the two patients'' groups for 13 out of the 22 miRNAs. The analysis of the Glioblastoma Multiforme (GBM) and Lower Grade Glioma (LGG) datasets from TCGA demonstrated the association with prognosis for 6 of those miRNAs. Moreover, in the GBM dataset miR-21 and miR-210 were predictors of worse prognosis in both univariable and multivariable Cox regression analyses (HR 1.19, p = 0.04, and HR 1.18, p = 0.029 respectively). Our results support a direct contribution of miRNAs to glioma cancerogenesis and suggest that miR-21 and miR-210 may play a role in the aggressive clinical behaviour of glioblastomas.     

Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction

Background

Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35–50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.

Methodology/Principal Findings

From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.

Conclusions/Significance

The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.     

Transcriptional landscape of DNA repair genes underpins a pan-cancer prognostic signature associated with cell cycle dysregulation and tumor hypoxia

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multi-cohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n = 16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (P < 0.0001), papillary renal cell (P = 0.0007), liver (P = 0.002), lung (P = 0.028), pancreas (P = 0.00013) or endometrial (P = 0.00063) cancers. Receiver operating characteristic analyses revealed that a combined model of the 10-gene signature and tumor staging outperformed either classifier when considered alone. Multivariate Cox regression models incorporating additional clinicopathological features showed that the signature was an independent predictor of overall survival. Tumor hypoxia is associated with adverse outcomes. Consistent across all six cancers, patients with high 10-gene and high hypoxia scores had significantly higher mortality rates compared to those with low 10-gene and low hypoxia scores. Functional enrichment analyses revealed that high mortality rates in patients with high 10-gene scores were attributable to an overproliferation phenotype. Death risk in these patients was further exacerbated by concurrent mutations of a cell cycle checkpoint protein, TP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.     

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, {"type":"entrez-geo","attrs":{"text":"GSE1993","term_id":"1993"}}GSE1993 and {"type":"entrez-geo","attrs":{"text":"GSE4422","term_id":"4422"}}GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.     

Development and validation of a 14-gene signature for prognosis prediction in hepatocellular carcinoma

Worldwide, hepatocellular carcinoma (HCC) remains a crucial medical problem. Precise and concise prognostic models are urgently needed because of the intricate gene variations among liver cancer cells. We conducted this study to identify a prognostic gene signature with biological significance. We applied two algorithms to generate differentially expressed genes (DEGs) between HCC and normal specimens in The Cancer Genome Atlas cohort (training set included) and performed enrichment analyses to expound on their biological significance. A protein-protein interactions network was established based on the STRING online tool. We then used Cytoscape to screen hub genes in crucial modules. A multigene signature was constructed by Cox regression analysis of hub genes to stratify the prognoses of HCC patients in the training set. The prognostic value of the multigene signature was externally validated in two other sets from Gene Expression Omnibus (GSE14520 and GSE76427), and its role in recurrence prediction was also investigated. A total of 2000 DEGs were obtained, including 1542 upregulated genes and 458 downregulated genes. Subsequently, we constructed a 14-gene signature on the basis of 56 hub genes, which was a good predictor of overall survival. The prognostic signature could be replicated in GSE14520 and GSE76427. Moreover, the 14-gene signature could be applied for recurrence prediction in the training set and GSE14520. In summary, the 14-gene signature extracted from hub genes was involved in some of the HCC-related signalling pathways; it not only served as a predictive signature for HCC outcome but could also be used to predict HCC recurrence.     

Development of a tumor microenvironment-related prognostic signature in glioma to predict immune landscape and potential therapeutic drugs

The involvement of the tumor microenvironment (TME) in the biology of gliomas has expanded, while it is yet uncertain its potential of supporting diagnosis and therapy choices. According to immunological characteristics and overall survival, cohorts of glioma patients from public databases were separated into two TME-relevant clusters in this analysis. Based on differentially expressed genes between TME clusters and correlative regression analysis, a 21-gene molecular classifier of TME-related prognostic signature (TPS) was constructed. Afterward, the prognostic efficacy and effectiveness of TPS were assessed in the training and validation groups. The outcome demonstrated that TPS might be utilized alone or in conjunction with other clinical criteria to act as a superior prognostic predictor for glioma. Also, high-risk glioma patients classified by TPS were considered to associate with enhanced immune infiltration, greater tumor mutation, and worse general prognosis. Finally, possible treatment medicines specialized for different risk subgroups of TPS were evaluated in drug databases.     

Prognostic significance of DNA ploidy and proliferation rate in human astrocytic gliomas

DNA ploidy and the proliferative potential in 75 gliomas were investigated using bromodeoxyuridine labelling index (BrdUrd LI), S-phase fraction (SPF) and argyrophilic nucleolar organizer regions (AgNOR) technique. There were 53 highly malignant (AIII-AIV), and 22 low-grade (AI-AII) gliomas. One fragment of the tumour was fixed in Carnoy's solution for AgNOR test, while the other fragments were used for flow cytometric determination of the labelling index, SPF and DNA ploidy. For the BrdUrdLI, tumour samples from each patient were incubated in vitro for one hour at 37 degrees C with BrdUrd using the high pressure oxygen method. The tumours showed variability in the BrdUrdLI values, SPF and AgNOR counts/cell nucleus. The same percentage of DNA aneuploidy (55%) was found in high-grade as well as in low-grade gliomas. Univariate analysis showed that patients with grade I & II gliomas had significantly higher 3-year survival rate (p = 0.0193) than those with grade III and grade IV gliomas. Also patients with lower proliferation rate of tumours (BrdUrdLI < or =2.3% and AgNOR counts < or =2.6%/cell) had higher 3-year survival rate (p<0.03), which can be helpful in prognosis. Tumour ploidy or SPF had no influence on patients' survival (p = 0.7908). Cox multivariate analysis showed that only patients' age > 45 years and high tumour grade (III and IV) were significant unfavourable prognostic factors in terms of patients' survival.