A five-gene expression signature to predict progression in T1G3 bladder cancer

ObjectiveThe aim of this study was to analyze tumour gene expression profiles of progressive and non-progressive T1G3 bladder cancer (BC) patients to develop a gene expression signature to predict tumour progression.MethodsRetrospective, multicenter study of 96 T1G3 BC patients without carcinoma in situ (CIS) who underwent a transurethral resection. Formalin-fixed paraffin-embedded tissue samples were collected. Global gene expression patterns were analyzed in 21 selected samples from progressive and non-progressive T1G3 BC patients using Illumina microarrays. Expression levels of 94 genes selected based on microarray data and based on literature were studied by quantitative polymerase chain reaction (qPCR) in an independent series of 75 progressive and non-progressive T1G3 BC patients. Univariate logistic regression was used to identify individual predictors. A variable selection method was used to develop a multiplex biomarker model. Discrimination of the model was measured by area under the receiver-operating characteristic curve. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin.ResultsA total of 1294 genes were found differentially expressed between progressive and non-progressive patients. Differential expression of 15 genes was validated by qPCR in an additional set of samples. A five-gene expression signature (ANXA10, DAB2, HYAL2, SPOCD1, and MAP4K1) discriminated progressive from non-progressive T1G3 BC patients with a sensitivity of 79% and a specificity of 86% (AUC = 0.83). Direct interactions between the five genes of the model were not found.ConclusionsProgressive and non-progressive T1G3 bladder tumours have shown different gene expression patterns. To identify T1G3 BC patients with a high risk of progression, a five-gene expression signature has been developed.     

miRNA expression profiling of inflammatory breast cancer identifies a 5‐miRNA signature predictive of breast tumor aggressiveness

IBC (inflammatory breast cancer) is a rare but very aggressive form of breast cancer with a particular phenotype. The molecular mechanisms responsible for IBC remain largely unknown. In particular, genetic and epigenetic alterations specific to IBC remain to be identified. MicroRNAs, a class of small noncoding RNAs able to regulate gene expression, are deregulated in breast cancer and may therefore serve as tools for diagnosis and prediction. This study was designed to determine miRNA expression profiling (microRNAome) in IBC. Quantitative RT‐PCR was used to determine expression levels of 804 miRNAs in a screening series of 12 IBC compared to 31 non‐stage‐matched non‐IBC and 8 normal breast samples. The differentially expressed miRNAs were then validated in a series of 65 IBC and 95 non‐IBC. From a set of 18 miRNAs of interest selected from the screening series, 13 were differentially expressed with statistical significance in the validation series of IBC compared to non‐IBC. Among these, a 5‐miRNA signature comprising miR‐421, miR‐486, miR‐503, miR‐720 and miR‐1303 was shown to be predictive for IBC phenotype with an overall accuracy of 89%. Moreover, multivariate analysis showed that this signature was an independent predictor of poor Metastasis‐Free Survival in non‐IBC patients.     

Identification of a glycolysis-related gene signature for predicting pancreatic cancer survival

BackgroundPancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract. Its clinical symptoms are obscure and atypical. It is difficult to diagnose and treat. Tumor cells mainly obtain energy through glycolysis to promote their growth. Inhibiting glycolysis can inhibit proliferation and kill tumor cells.MethodsUsing bioinformatics method, we investigate the relationships between glycolysis-related genes and PC tumor samples’ epidemiologic information comprehensively.ResultsDifferent expression levels of 27 genes were identified. Using bioinformatics methods, we plotted two subgroup curves based on glycolysis-related gene expression level. Potential predictive genes were screened and their prognostic values were analyzed. Survival among high-risk group and low risk group had significant difference. Receiver operating characteristic (ROC) curve analysis indicated that area under curve (AUC) of 10 genes was greater than 0.8. These genes could be used for clinical diagnosis and prediction for PC. Two potential predictors [Kinesin Family Member 20A (KIF20A) and MET Proto-Oncogene, Receptor Tyrosine Kinase (MET)] that met the independent predictive value were selected. In univariate analysis, we screened out 3 regulators MET, protein kinase CAMP-activated catalytic subunit alpha (PRKACA) and KIF20A. According to the 3 regulatory factors, the prognostic signals of PC were constructed, by which the samples with good prognosis and poor prognosis can be clearly distinguished independently of potential confounding factors.ConclusionsOur results indicate that for PC, glycolysis -related genes could be promising therapeutic targets or prognostic indicators.     

Immune-related gene signature in predicting prognosis of early-stage colorectal cancer patients

AimImmune-related genes are associated with the prognosis of colorectal cancer (CRC) patients. The aim of this study was to evaluate the impact of an immune-related gene signature (IRGS) in predicting the prognosis of early-stage CRC patients.MethodsIn total, 309 CRC patients were selected for the identification of prognostic IRGS using the CIT/GSE39582 microarray dataset. Five independent datasets including 1587 CRC patients were divided into a training cohort (n = 566) and two validation cohorts (n = 624 in validation-1 and n = 397 in meta-validation). Prognostic analyses were performed to test the predictive value of IRGS.ResultsA prognostic IRGS that included 23 immune-related genes was constructed and significantly stratified patients into immune low-vs. high-risk groups in terms of disease-free survival using patients with early-stage disease (I or II) in the training cohort. Similarly, a higher IRGS was correlated with significantly worse prognosis of early-stage patients in validation-1 and meta-validation cohorts. Compared with Oncotype DX colon, we found that IRGS exhibited an improved survival correlation in the training cohort. After integration with clinical characteristics, IRGS remained as an independent prognostic factor in multivariate analysis. Furthermore, IRGS-stratified immune low-risk group patients gained less benefit from adjuvant chemotherapy in the validation-1 cohort. Several biological processes, including inflammatory response, were enriched among genes in identified the immune high-risk group. Consistent with this finding, the IRGS-identified immune high-risk group exhibited significantly increased immune and stromal cell infiltration.ConclusionThe proposed prognostic IRGS is a promising system for estimating DFS of colorectal cancer patients, especially those with early-stage disease.     

A four-long non-coding RNA signature in predicting breast cancer survival

Background

Many long non-coding RNAs(lncRNAs) have been found to be a good marker for several tumors. Using lncRNA-mining approach, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival.

Methods

We performed LncRNA expression profiling in 887 breast cancer patients from Gene Expression Omnibus (GEO) datasets. The association between lncRNA signature and clinical survival was analyzed using the training set(n = 327, from GSE 20685). The validation for the association was performed in another three independent testing sets(252 from {"type":"entrez-geo","attrs":{"text":"GSE21653","term_id":"21653"}}GSE21653, 204 from {"type":"entrez-geo","attrs":{"text":"GSE12276","term_id":"12276"}}GSE12276, and 104 from {"type":"entrez-geo","attrs":{"text":"GSE42568","term_id":"42568"}}GSE42568).

Results

A set of four lncRNA genes ({"type":"entrez-nucleotide","attrs":{"text":"U79277","term_id":"1710245","term_text":"U79277"}}U79277, {"type":"entrez-nucleotide","attrs":{"text":"AK024118","term_id":"10436421","term_text":"AK024118"}}AK024118, {"type":"entrez-nucleotide","attrs":{"text":"BC040204","term_id":"25955567","term_text":"BC040204"}}BC040204, {"type":"entrez-nucleotide","attrs":{"text":"AK000974","term_id":"7021969","term_text":"AK000974"}}AK000974) have been identified by the random survival forest algorithm. Using a risk score based on the expression signature of these lncRNAs, we separated the patients into low-risk and high-risk groups with significantly different survival times in the training set. This signature was validated in the other three cohorts. Further study revealed that the four-lncRNA expression signature was independent of age and subtype. Gene Set Enrichment Analysis (GSEA) suggested that gene sets were involved in several cancer metastasis related pathways.

Conclusions

These findings indicate that lncRNAs may be implicated in breast cancer pathogenesis. The four-lncRNA signature may have clinical implications in the selection of high-risk patients for adjuvant therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-014-0084-7) contains supplementary material, which is available to authorized users.     

A 41-gene signature derived from breast cancer stem cells as a predictor of survival

Purpose

The aim of this study was to evaluate the ability of a 41-gene signature derived from breast cancer stem cells (BCSCs) to estimate the risk of metastasis and survival in breast cancer patients.

Methods

The centroid expression of the 41-gene signature derived from BCSCs was applied as the threshold to classify patients into two separate groups—patients with high expression (high-EL) of the prognostic signature and patients with low expression (low-EL). The predictive ability of the 41-gene signature was evaluated by Cox regression model and was compared against other popular tests, such as Oncotype and MammaPrint.

Results

Our results showed that the 41-gene prognostic signature was significantly associated with age (P = .0351) and ER status (P = .0095). The analysis indicated that patients in the high-EL group had a worse prognosis than those in the low-EL group in terms of both overall survival (OS: HR, 2.05, P = .009) and distant metastasis-free survival (DMFS: HR, 2.24, P = .002). Additionally, the 41-gene signature was an independent risk factor and separates patients based on estrogen receptor status. While comparable to Oncotype, the analysis demonstrated that the 41-gene signature had a better prognostic value in predicting DMFS and OS than AOL, NPI, St. Gallen, Veridex, and MammaPrint.

Conclusions

This study confirms the utility of the 41-gene signature and adds to the growing evidence that gene expression signatures of BCSCs have clinical potential to predict patient outcome and aid in treatment choice.     

miR-19a acts as an oncogenic microRNA and is up-regulated in bladder cancer

Background

The application of microRNAs (miRNAs) as potential biomarkers and therapy targets has been widely investigated in many kinds of cancers. The discovery of tumor associated miRNAs in serum of patients supported the use of plasma/serum miRNAs as noninvasive means of cancer detection. However, the aberrant expression of miRNAs in bladder cancer patients and their intensive roles and mechanisms in bladder cancer are poorly understood.

Methods

Taqman probe stem-loop real-time PCR was used to accurately measure the levels of miR-19a in bladder cancer cell lines, 100 pairs of bladder cancer tissues and the adjacent non-neoplastic tissues and also the plasma collected from bladder cancer patients and normal controls. miR-19a mimics and inhibitors were transfected into bladder cancer cells to investigate its role on regulating cell proliferation which was measured by CCK-8 and colony formation assay. The target of miR-19a was identified by western blot and whether its regulatory role depends on its target was improved by a rescue experiment with miR-19a mimic and PTEN expression plasmid.

Results

miR-19a was significantly up-regulated in bladder cancer tissues and high-level of miR-19a was correlative with more aggressive phenotypes of bladder cancer. Meanwhile, gain or loss of function of miR-19a demonstrated that miR-19a can promote cell growth of bladder cancer cells and the further mechanism studies indicated that its oncogenic role was dependent on targeting PTEN. Furthermore, investigation of miR-19a expression in the plasma of bladder cancer patients showed that miR-19a was also increased in plasma of bladder cancer patients which strongly supported miR-19a could be developed as potential diagnostic marker of bladder cancer.

Conclusions

Our data indicated that miR-19a might act as an oncogenic microRNA in bladder cancer and was significantly up-regulated in bladder cancer carcinogenesis. The oncogenic role of miR19a in bladder cancer was dependent on targeting PTEN.     

KIF11: A potential prognostic biomarker for predicting bone metastasis-free survival of prostate cancer

Most prostate cancer (PCa) cases remain indolent with a relatively good prognosis. However, bone metastasis of PCa can quickly worsen prognoses and lead to mortality. Metastasis-free survival (MFS), a strong surrogate for overall survival, is widely used in PCa prognosis research. The present study identified molecules that affect bone MFS in PCa, with clinical validation. Three datasets ({"type":"entrez-geo","attrs":{"text":"GSE32269","term_id":"32269"}}GSE32269, {"type":"entrez-geo","attrs":{"text":"GSE74367","term_id":"74367"}}GSE74367 and {"type":"entrez-geo","attrs":{"text":"GSE77930","term_id":"77930"}}GSE77930) were downloaded from the Gene Expression Omnibus database. Hub genes most relevant to clinical traits (bone metastasis-associated morbidity) were identified by weighted gene co-expression network analysis (WGCNA) and subjected to logistic regression analysis. Patient samples were obtained between January 2014 and December 2016, with a clinically annotated follow-up in December 2021. Clinical data and follow-up information for 60 patients with PCa were used in MFS analysis. Tumor samples were retrieved, and immunohistochemistry was performed to detect vascular endothelial growth factor (VEGF). The prognostic potential of the two molecules was assessed using Cox proportional hazards regression analysis. A total of 16 gene modules were obtained via WGCNA, and the tan module, containing 147 genes, was most closely linked to bone metastasis. In total, 877 differentially expressed genes (DEGs) were detected. The DEG-tan module intersection yielded seven hub genes [BUB1, kinesin family member (KIF)2C, RACGAP1, CENPE, KIF11, TTK and KIF20A]. Using univariate and multivariate logistic regression analyses for independent risk factors of bone metastasis, KIF11 and VEGF were found to be significantly associated with a higher T stage, prostate-specific antigen level and Gleason score. In addition, KIF11 and VEGF expression levels were positively correlated (P<0.001). Using univariate Cox analysis, KIF11 and VEGF were found to exhibit a significant association with poor MFS (P<0.05). However, only KIF11 was significantly associated with MFS upon multivariate analysis (P=0.007; hazard ratio, 2.776; 95% confidence interval, 1.315-5.859). Markers of bone metastasis in PCa were identified. Overall, KIF11 is an independent indicator that can predict bone metastasis for patients with PCa, which could be used to guide clinical practice.     

Whole blood-derived miRNA profiles as potential new tools for ovarian cancer screening

Background:

Screening is an unsolved problem for ovarian cancer (OvCA). As late detection is equivalent to poor prognosis, we analysed whether OvCA patients show diagnostically meaningful microRNA (miRNA) patterns in blood cells.

Methods:

Blood-borne whole miRNome profiles from 24 patients with OvCA and 15 age- and sex-matched healthy controls were biostatistically evaluated.

Results:

Student''s t-test revealed 147 significantly deregulated miRNAs before and 4 after Benjamini–Hochberg adjustment. Although these included miRNAs already linked to OvCA (e.g., miR-16, miR-155), others had never before been connected to specific diseases. A bioinformatically calculated miRNA profile allowed for discrimination between blood samples of OvCA patients and healthy controls with an accuracy of >76%. When only cancers of the serous subtype were considered and compared with an extended control group (n=39), accuracy, specificity and sensitivity all increased to >85%.

Conclusion:

Our proof-of-principle study strengthens the hypothesis that neoplastic diseases generate characteristic miRNA fingerprints in blood cells. Still, the obtained OvCA-associated miRNA pattern is not yet sensitive and specific enough to permit the monitoring of disease progression or even preventive screening. Microarray-based miRNA profiling from peripheral blood could thus be combined with other markers to improve the notoriously difficult but important screening for OvCA.     

非编码RNA在膀胱癌中的研究进展

膀胱癌是全世界最常见的癌症之一,虽然手术结合化疗和放疗在一定程度上改善了患者的预后,但大多数肌肉浸润性膀胱癌患者的预后仍然很差。而且,膀胱癌的确切机制和关键调节因子尚不清楚。最近的研究表明,一些非编码RNA在膀胱癌中表达失调,对膀胱癌的发生发展起着至关重要的作用。更重要的是,非编码RNA可作为膀胱癌的诊断和预后的生物标志物。本综述旨在总结近期的研究进展,这些研究表明了包括miRNA、lncRNA、circRNA在内的三类非编码RNA在膀胱癌中的调控机制,以及它们作为临床诊断或预后生物标志物的潜在作用。     

An ER-associated miRNA signature predicts prognosis in ER-positive breast cancer

Background

Breast cancer patients with positive estrogen receptor (ER) have a better prognosis. However, no prognostic miRNA signature was reported in the ER-positive breast cancer. The aim of the study was to identify and assess the prognostic significance of a miRNA signature in ER-positive breast cancer.

Methods

Two cohorts from The Cancer Genome Atlas (TCGA) dataset were used as training (n =596) and testing set (n =319). Differential expression profiling was identified in the training set. And the prognostic value of the miRNA signature was then assessed in the two cohorts.

Results

A total of 14 miRNAs were observed to be associated with the status of ER by significance analysis of microarrays (SAM) in the training set. Patients were characterized as high score or low score group according to the calculated risk scores from each miRNA. And patients in high score group had worse overall survival compared with those in low score group both in the training and testing set.

Conclusions

Our study revealed a miRNA signature including 14 miRNAs associated with ER status which could act as a prognostic marker in ER-positive breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-014-0094-5) contains supplementary material, which is available to authorized users.     

Urinary cell-free microRNA biomarker could discriminate bladder cancer from benign hematuria

The most common symptom of bladder cancer (BC) is hematuria. However, not all patients with hematuria are diagnosed with BC. Here, we explored a novel method to discriminate BC from hematuria under nonmalignant conditions by measuring differences in urinary cell-free microRNA (miRNA) expression between patients with BC and those with hematuria. A multicenter study was performed using 543 urine samples obtained from the National Biobank of Korea, including 326 BC, 174 hematuria and 43 pyuria without cancer. The urinary miR-6124 to miR-4511 ratio was considerably higher in BC than in hematuria or pyuria, and enabled the discrimination of BC from patients with hematuria at a sensitivity of >90% (p < 0.001). Conclusively, the proposed noninvasive diagnostic tool based on the expression ratio of urinary cell-free miR-6124 to miR-4511 can reduce unnecessary cystoscopies in patients with hematuria undergoing evaluation for BC, with a minimal loss in sensitivity for detecting cancer.     

Development of a metabolism-related signature for predicting prognosis,immune infiltration and immunotherapy response in breast cancer

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8⁺ T cells, CD4⁺ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.     

A microRNA gene signature for identification of lung cancer

BackgroundLung cancer is the leading cause of cancer deaths worldwide, compounded by late diagnosis. MicroRNAs (miRNA) are recently discovered short, noncoding genes that play essential roles in tissue differentiation during normal development and tumorigenesis. miRNA profiles across all histologic grades can provide a reliable and standardized method for the identification of lung cancer.MethodsA microRNA lung cancer dataset was analyzed. Differentially expressed microRNAs were obtained post-normalization of data using t-test (p < 0.01). The data for differentially expressed microRNAs were processed using K-nearest neighbors classification method to obtain unique miRNAs expression patterns. The predicted mRNA targets were identified using TargetScan and the molecular functions associated with the predicted targets were retrieved from the Gene Ontology Consortium and represented using GO IDs in a directed acyclic graph.ResultsThe results indicate that lung cancer samples can be classified using a small panel of 19 unique microRNAs (8 down-regulated and 11 up-regulated) with over 85% classification accuracy. Furthermore, using classical enrichment analysis, this study identified 66 molecular function groups which are potentially the functional signaling pathways altered by these differentially expressed microRNAs.ConclusionsWe identified a microRNA gene signature representative of functioning as a diagnostic biomarker for lung cancer. These findings can potentially form the basis for the development of a standardized diagnostic assay that can be used for early diagnosis of lung cancer equally well from resection specimens and cytology samples.     

TPX2基因表达对膀胱癌患者预后的影响

目的 通过基因表达汇编公共数据库,探讨 Xklp2 靶蛋白在膀胱癌中的表达情况,并进一步探索其与膀胱癌临床 病理特征的关系,评价TPX2 对膀胱癌术后患者预后评价的意义,预测TPX2 推动膀胱癌发生和发展机制。方法 从 NCBI 的基因表达汇编数据库收集膀胱肿瘤相关的公共数据集,对其中的表达谱资料及相关临床资料进行分析;基于GSEA3.0, 使用基因富集分析方法,分析TPX2 调控的基因通路。结果 TPX2 在膀胱癌组织中为高表达（P ＜ 0.0001）。不同年龄 （P=0.027）、性别（P=0.040）、T 分期（P=0.001）、N 分期（P=0.013）、进程（progression,P=0.002）和分级水 平（grade）中,TPX2 的表达存在明显差异。TPX2 的表达水平与膀胱癌患者预后总生存率和是否复发相关（P ＜ 0.05）; TPX2 表达水平高的样本富集了与精子发生、未折叠蛋白反应、PI3K/AKT/mTOR 通路、mtorc1 信号通路、胆固醇平衡、有丝分裂、糖酵解、G2M 检查点、E2F 转录因子、癌基因 myc 有关的基因集。结论 TPX2 在膀胱癌中为高表达,与膀胱癌多 个病理性指标相关,且可以作为潜在的判断膀胱癌患者预后的标志物和治疗肿瘤的靶标。