Comprehensive analysis of mRNA‐level and miRNA‐level subpathway activities for identifying robust ovarian cancer prognostic signatures

Ovarian cancer (OvCa) causes the highest mortality among all gynaecologic cancers. A large number of mRNA‐ or miRNA‐based signatures were identified for OvCa patient prognosis. However, the comprehensive analysis of function‐level prognostic signatures is currently not considered in OvCa. In the present study, we respectively inferred subpathway activities from mRNA and miRNA levels based on high‐throughput expression profiles and reconstructed subpathways. Firstly, the activities of two tumour pathways were calculated and the difference between normal and tumour samples were analysed using multiple tumour types. Then, we calculated subpathway activities for OvCa based on the expression profiles from both mRNA and miRNA levels. Furthermore, based on these subpathway activity matrices, we performed bootstrap analysis to obtain sub‐training sets and utilized univariate method to identify robust OvCa prognostic subpathways. A comprehensive comparison of subpathway results between these two levels was performed. As a result, we observed subpathway mutual exclusion trend between the levels of mRNA and miRNA, which indicated the necessary of combining mRNA‐miRNA levels. Finally, by using ICGC data as testing sets, we utilized two strategies to verify survival predictive power of the mRNA‐miRNA combined subpathway signatures and performed comparisons with results from individual levels. It was confirmed that our framework displayed application to identify robust and efficient prognostic signatures for OvCa, and the combined signatures indeed exhibited advantages over individual ones. In the study, we took a step forward in relevant novel integrated functional signatures for OvCa prognosis.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

A five‐microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower‐grade glioma

Despite the prognostic value of IDH and other gene mutations found in diffuse glioma, markers that judge individual prognosis of patients with diffuse lower‐grade glioma (LGG) are still lacking. This study aims to develop an expression‐based microRNA signature to provide survival and radiotherapeutic response prediction for LGG patients. MicroRNA expression profiles and relevant clinical information of LGG patients were downloaded from The Cancer Genome Atlas (TCGA; the training group) and the Chinese Glioma Genome Atlas (CGGA; the test group). Cox regression analysis, random survival forests‐variable hunting (RSFVH) screening and receiver operating characteristic (ROC) were used to identify the prognostic microRNA signature. ROC and TimeROC curves were plotted to compare the predictive ability of IDH mutation and the signature. Stratification analysis was conducted in patients with radiotherapy information. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore the biological function of the signature. We identified a five‐microRNA signature that can classify patients into low‐risk or high‐risk group with significantly different survival in the training and test datasets (P < 0.001). The five‐microRNA signature was proved to be superior to IDH mutation in survival prediction (AUCtraining = 0.688 vs 0.607). Stratification analysis found the signature could further divide patients after radiotherapy into two risk groups. GO and KEGG analyses revealed that microRNAs from the prognostic signature were mainly enriched in cancer‐associated pathways. The newly discovered five‐microRNA signature could predict survival and radiotherapeutic response of LGG patients based on individual microRNA expression.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

A novel prognostic signature of immune‐related genes for patients with colorectal cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with an estimated 1.8 million new cases worldwide and associated with high mortality rates of 881 000 CRC‐related deaths in 2018. Screening programs and new therapies have only marginally improved the survival of CRC patients. Immune‐related genes (IRGs) have attracted attention in recent years as therapeutic targets. The aim of this study was to identify an immune‐related prognostic signature for CRC. To this end, we combined gene expression and clinical data from the CRC data sets of The Cancer Genome Atlas (TCGA) into an integrated immune landscape profile. We identified a total of 476 IRGs that were differentially expressed in CRC vs normal tissues, of which 18 were survival related according to univariate Cox analysis. Stepwise multivariate Cox proportional hazards analysis established an immune‐related prognostic signature consisting of SLC10A2, FGF2, CCL28, NDRG1, ESM1, UCN, UTS2 and TRDC. The predictive ability of this signature for 3‐ and 5‐year overall survival was determined using receiver operating characteristics (ROC), and the respective areas under the curve (AUC) were 79.2% and 76.6%. The signature showed moderate predictive accuracy in the validation and GSE38832 data sets as well. Furthermore, the 8‐IRG signature correlated significantly with tumour stage, invasion, lymph node metastasis and distant metastasis by univariate Cox analysis, and was established an independent prognostic factor by multivariate Cox regression analysis for CRC. Gene set enrichment analysis (GSEA) revealed a relationship between the IRG prognostic signature and various biological pathways. Focal adhesions and ECM‐receptor interactions were positively correlated with the risk scores, while cytosolic DNA sensing and metabolism‐related pathways were negatively correlated. Finally, the bioinformatics results were validated by real‐time RT?qPCR. In conclusion, we identified and validated a novel, immune‐related prognostic signature for patients with CRC, and this signature reflects the dysregulated tumour immune microenvironment and has a potential for better CRC patient management.     

An EMT‐related gene signature for the prognosis of human bladder cancer

The transition from non–muscle‐invasive bladder cancer (NMIBC) to muscle‐invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial‐mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT‐related gene signature in BLCA. The patients with high risk scores had significantly worse overall survival (OS) and disease‐free survival (DFS) than those with low risk scores. The EMT‐related gene signature also performed favourably in the accuracy of prognosis and in the subtype survival analysis. Univariate and multivariate Cox regression analyses demonstrated that the EMT‐related gene signature, pathological N stage and age were independent prognostic factors for predicting survival in BLCA patients. Furthermore, the predictive nomogram model was able to effectively predict the outcome of BLCA patients by appropriately stratifying the risk score. In conclusion, we developed a novel EMT‐related gene signature that has tumour‐promoting effects, acts as a negative independent prognostic factor and might facilitate personalized counselling and treatment in BLCA.     

The avidity of tumor‐specific T cells amplified by a plasmacytoid dendritic cell‐based assay can predict the clinical evolution of melanoma patients

The advent of immune checkpoint blockers and targeted therapies has changed the outcome of melanoma. However, many patients experience relapses, emphasizing the need for predictive and prognostic biomarkers. We developed a strategy based on plasmacytoid dendritic cells (pDCs) loaded with melanoma tumor antigens that allows eliciting highly efficient antitumor T‐cell responses. We used it to investigate antitumor T‐cell functionality in peripheral blood mononuclear cells and tumor‐infiltrating lymphocytes from melanoma patients. The pDCs elicited tumor‐specific T cells in different proportions and displaying diverse functional features, dependent upon the stage of the disease, but independent of the histological parameters at diagnosis. Strikingly, the avidity of the MelA‐specific T cells triggered by the pDCs was found to predict patient relapse time and overall survival. Our findings highlighted unexplored aspects of antitumor T‐cell responsiveness in melanoma, and revealed for the first time the structural avidity of tumor‐specific T cells as a crucial feature for predicting clinical evolution.     

Inference of sex‐specific expansion patterns in human populations from Y‐chromosome polymorphism

Studying the current distribution of genetic diversity in humans has important implications for our understanding of the history of our species. We analyzed a set of linked STR and SNP loci from the paternally inherited Y chromosome to infer the past demography of 55 African and Eurasian populations, using both the parametric and nonparametric coalescent‐based methods implemented in the BEAST application. We inferred expansion events in most sedentary farmer populations, while we found constant effective population sizes for both nomadic hunter‐gatherers and seminomadic herders. Our results differed, on several aspects, from previous results on mtDNA and autosomal markers. First, we found more recent expansion patterns in Eurasia than in Africa. This discrepancy, substantially stronger than the ones found with the other kind of markers, may result from a lower effective population size for men, which might have made male‐transmitted markers more sensitive to the out‐of‐Africa bottleneck. Second, we found expansion signals only for sedentary farmers but not for nomadic herders in Central Asia, while these signals were found for both kind of populations in this area when using mtDNA or autosomal markers. Expansion signals in this area may result from spatial expansion processes and may have been erased for the Y chromosome among the herders because of restricted male gene flow. Am J Phys Anthropol 157:217–225, 2015. © 2015 Wiley Periodicals, Inc.     

Development and validation of a novel 15‐CpG‐based signature for predicting prognosis in triple‐negative breast cancer

DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence. Increasing studies have revealed that DNA methylation data play a vital role in the field of oncology. However, the methylation site signature in triple‐negative breast cancer (TNBC) remains unknown. In our research, we analysed 158 TNBC samples and 98 noncancerous samples from The Cancer Genome Atlas (TCGA) in three phases. In the discovery phase, 86 CpGs were identified by univariate Cox proportional hazards regression (CPHR) analyses to be significantly correlated with overall survival (P < 0.01). In the training phase, these candidate CpGs were further narrowed down to a 15‐CpG‐based signature by conducting least absolute shrinkage and selector operator (LASSO) Cox regression in the training set. In the validation phase, the 15‐CpG‐based signature was verified using two different internal sets and one external validation set. Furthermore, a nomogram comprising the CpG‐based signature and TNM stage was generated to predict the 1‐, 3‐ and 5‐year overall survival in the primary set, and it showed excellent performance in the three validation sets (concordance indexes: 0.924, 0.974 and 0.637). This study showed that our nomogram has a precise predictive effect on the prognosis of TNBC and can potentially be implemented for clinical treatment and diagnosis.     

Proteomic analysis and abrogated expression of O‐GlcNAcylated proteins associated with primary breast cancer

O‐GlcNAcylation is a dynamic PTM of nuclear and cytoplasmic proteins, regulated by O‐GlcNAc transferase (OGT) and O‐GlcNAcase, which catalyze the addition and removal of O‐GlcNAc, respectively. This modification is associated with glucose metabolism, which plays important roles in many diseases including cancer. Although emerging evidence reveals that some tumor‐associated proteins are O‐GlcNAc modified, the total O‐GlcNAcylation in cancer is still largely unexplored. Here, we demonstrate that O‐GlcNAcylation was increased in primary breast malignant tumors, not in benign tumors and that this augmentation was associated with increased expression of OGT level. Using 2D O‐GlcNAc immnoblotting and LC‐MS/MS analysis, we successfully identified 29 proteins, with seven being uniquely O‐GlcNAcylated or associated with O‐GlcNAcylation in cancer. Of these identified proteins, some were related to the Warburg effect, including metabolic enzymes, proteins involved in stress responses and biosynthesis. In addition, proteins associated with RNA metabolism, gene expression, and cytoskeleton were highly O‐GlcNAcylated or associated with O‐GlcNAcylation. Moreover, OGT knockdown showed that decreasing O‐GlcNAcylation was related to inhibition of the anchorage‐independent growth in vitro. These data indicate that aberrant protein O‐GlcNAcylation is associated with breast cancer. Abnormal modification of these O‐GlcNAc‐modified proteins might be one of the vital malignant characteristics of cancer.     

A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and {"type":"entrez-geo","attrs":{"text":"GSE31811","term_id":"31811"}}GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, {"type":"entrez-nucleotide","attrs":{"text":"AC007277.1","term_id":"4580503","term_text":"AC007277.1"}}AC007277.1, AC005324.4, {"type":"entrez-nucleotide","attrs":{"text":"AL512506.1","term_id":"12044702","term_text":"AL512506.1"}}AL512506.1, {"type":"entrez-nucleotide","attrs":{"text":"AC068790.7","term_id":"8571647","term_text":"AC068790.7"}}AC068790.7, {"type":"entrez-nucleotide","attrs":{"text":"AC022509.2","term_id":"6938434","term_text":"AC022509.2"}}AC022509.2, {"type":"entrez-nucleotide","attrs":{"text":"AC113139.1","term_id":"18875234","term_text":"AC113139.1"}}AC113139.1, LINC00106, {"type":"entrez-nucleotide","attrs":{"text":"AC005165.1","term_id":"3242748","term_text":"AC005165.1"}}AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.     

Tracing site‐specific isotopic signatures along a Blue Tit Cyanistes caeruleus food chain

Food chains culminating with temperate insectivorous passerines are well described, yet whether trophic webs can be site‐specific remains a largely unexplored question. In the case of site‐ or habitat‐specificity of food webs, stable isotope signatures of bird feathers may enable assignment of unmarked individuals to a site or a habitat of origin. We address this question in landscapes that include contrasting forest habitat patches with either deciduous Downy Oak Quercus humilis or evergreen Holm Oak Quercus ilex as dominant tree species. First, we examine the spatial variation across habitats and sites in the stable isotope ratios of carbon (δ¹³C) and nitrogen (δ¹⁵N) along the oak leaf–Tortrix moth Tortrix viridana caterpillar–Blue Tit Cyanistes caeruleus food chain. Secondly, we assess whether the isotopic signatures allow for correct assignment of individual birds to their site or habitat of origin. At the scale of the landscape, stable isotope values enabled identification of the different components of the Blue Tit food chain: from oak leaves to Blue Tit nestlings and yearling birds. However, isotopic signatures were site‐specific (i.e. geographical) more than habitat‐specific (i.e. deciduous vs. evergreen oaks). Discriminant analyses correctly assigned 85% of nestlings and 83% of resident yearling birds, indicating a pronounced effect of site on Blue Tit feather isotopic signatures. We thereby demonstrate that isotopes reflect a stronger association of locally born birds to the local features of their habitat than that of un‐ringed yearling birds, whose plumage may have grown while in a wider geographical area. This study provides evidence of site‐specific isotopic signatures from oak leaves to Blue Tit feathers at a fine spatial scale.     

Combined analysis of RNA‐sequence and microarray data reveals effective metabolism‐based prognostic signature for neuroblastoma

The relationship between metabolism reprogramming and neuroblastoma (NB) is largely unknown. In this study, one RNA‐sequence data set (n = 153) was used as discovery cohort and two microarray data sets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed metabolic genes were identified by comparing stage 4s and stage 4 NBs. Twelve metabolic genes were selected by LASSO regression analysis and integrated into the prognostic signature. The metabolic gene signature successfully stratifies NB patients into two risk groups and performs well in predicting survival of NB patients. The prognostic value of the metabolic gene signature is also independent with other clinical risk factors. Nine metabolism‐related long non‐coding RNAs (lncRNAs) were also identified and integrated into the metabolism‐related lncRNA signature. The lncRNA signature also performs well in predicting survival of NB patients. These results suggest that the metabolic signatures have the potential to be used for risk stratification of NB. Gene set enrichment analysis (GSEA) reveals that multiple metabolic processes (including oxidative phosphorylation and tricarboxylic acid cycle, both of which are emerging targets for cancer therapy) are enriched in the high‐risk NB group, and no metabolic process is enriched in the low‐risk NB group. This result indicates that metabolism reprogramming is associated with the progression of NB and targeting certain metabolic pathways might be a promising therapy for NB.     

Coupling proteomics and transcriptomics in the quest of subtype‐specific proteins in breast cancer

Breast‐cancer subtypes present with distinct clinical characteristics. Therefore, characterization of subtype‐specific proteins may augment the development of targeted therapies and prognostic biomarkers. To address this issue, MS‐based secretome analysis of eight breast cancer cell lines, corresponding to the three main breast cancer subtypes was performed. More than 5200 non‐redundant proteins were identified with 23, four, and four proteins identified uniquely in basal, HER2‐neu‐amplified, and luminal breast cancer cells, respectively. An in silico mRNA analysis using publicly available breast cancer tissue microarray data was carried out as a preliminary verification step. In particular, the expression profiles of 15 out of 28 proteins included in the microarray (from a total of 31 in our subtype‐specific signature) showed significant correlation with estrogen receptor (ER) expression. A MS‐based analysis of breast cancer tissues was undertaken to verify the results at the proteome level. Eighteen out of 31 proteins were quantified in the proteomes of ER‐positive and ER‐negative breast cancer tissues. Survival analysis using microarray data was performed to examine the prognostic potential of these selected candidates. Three proteins correlated with ER status at both mRNA and protein levels: ABAT, PDZK1, and PTX3, with the former showing significant prognostic potential.