A robust six-miRNA prognostic signature for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a major health problem worldwide. We aimed to identify a robust microRNA (miRNA)-based signature for predicting HNSCC prognosis. The miRNA expression profiles of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA HNSCC cohort was randomly divided into the discovery and validation cohort. A miRNA-based prognostic signature was built up based on TGCA discovery cohort, and then further validated. The downstream targets of prognostic miRNAs were subjected to functional enrichment analyses. The role of miR-1229-3p, a prognosis-related miRNA, in tumorigenesis of HNSCC was further evaluated. A total of 305 significantly differentially expressed miRNAs were found between HNSCC samples and normal tissues. A six-miRNA prognostic signature was constructed, which exhibited a strong association with overall survival (OS) in the TCGA discovery cohort. In addition, these findings were successfully confirmed in TCGA validation cohort and our own independent cohort. The miRNA-based signature was demonstrated as an independent prognostic indicator for HNSCC. A risk signature-based nomogram model was constructed and showed good performance for predicting the OS for HNSCC. The functional analyses revealed that the downstream targets of these prognostic miRNAs were closely linked to cancer progression. Mechanistically, in vitro analysis revealed that miR-1229-3p played a tumor promoting role in HNSCC. In conclusion, our study has developed a robust miRNA-based signature for predicting the prognosis of HNSCC with high accuracy, which will contribute to improve the therapeutic outcome.     

A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients

The abnormal expression of microRNAs (miRNAs) or protein-coding genes (PCGs) have been found to be associated with the prognosis of hepatocellular carcinoma (HCC) patients. Using bioinformatics analysis methods including Cox’s proportional hazards regression analysis, the random survival forest algorithm, Kaplan–Meier, and receiver operating characteristic (ROC) curve analysis, we mined the gene expression profiles of 469 HCC patients from The Cancer Genome Atlas (n = 379) and Gene Expression Omnibus (GSE14520; n = 90) public database. We selected a signature comprising one protein-coding gene (PCG; DNA polymerase μ) and three miRNAs (hsa-miR-149-5p, hsa-miR-424-5p, hsa-miR-579-5p) with highest accurate prediction (area under the ROC curve [AUC] = 0.72; n = 189) from the training data set. The signature stratified patients into high- and low-risk groups with significantly different survival (median 27.9 vs. 55.2 months, log-rank test, p < 0.001) in the training data set, and its risk stratification ability were validated in the test data set (median 47.4 vs. 84.4 months, log-rank test, p = 0.03) and an independent data set (median 31.0 vs. 46.0 months, log-rank test, p = 0.01). Multivariable Cox regression analysis showed that the signature was an independent prognostic factor. And the signature was proved to have a better survival prediction power than tumor–node–metastasis (TNM) stage (AUC _signature = 0.72/0.64/0.62 vs. AUC _TNM = 0.65/0.61/0.61; p < 0.05). Moreover, we validated the expression of these prognostic genes from the PCG-miRNA signature in Huh-7 cell by real-time polymerase chain reaction. In conclusion, we found a signature that can predict survival of HCC patients and serve as a prognostic marker for HCC.     

DNA methylation profiling to predict recurrence risk in stage Ι lung adenocarcinoma: Development and validation of a nomogram to clinical management

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.     

Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis

MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer.     

Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Current studies suggest that some microRNAs (miRNAs) are associated with prognosis in clear cell renal cell carcinoma (ccRCC). In this paper, we aimed to identify a miRNAs signature to improve prognostic prediction for ccRCC patients. Using ccRCC RNA-Seq data of The Cancer Genome Atlas (TCGA) database, we identified 177 differentially expressed miRNAs between ccRCC and paracancerous tissue. Then all the ccRCC tumor samples were divided into training set and validation set randomly. Three-miRNA signature including miR130b, miR-18a, and miR-223 were constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression model in training set. According to optimal cut-off value of three-miRNA signature risk score, all the patients could be classified into high-risk group and low-risk group significantly. Survival of patients was significantly different between two groups (hazard ratio, 5.58, 95% confidence interval, 3.17-9.80; P < 0.0001), and three-miRNA signature performed favorably prognostic and predictive accuracy. The results were further validated in the validation set and total set. Multivariate Cox regression analyses and subgroup analyses showed that three-miRNA signature was an independent prognostic factor. Two nomograms that integrated three-miRNA signature and three clinicopathological risk factors were constructed to predict overall survival and disease-free survival after surgery for ccRCC patients. Functional enrichment analysis showed the possible roles of three-miRNA signature in some cancer-associated biological processes and pathways. In conclusion, we developed a novel three-miRNA signature that performed reliable prognostic for patient survival with ccRCC, it might facilitate ccRCC patients counseling and individualize management.     

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.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

术前血浆D-二聚体水平及血小板计数与局部晚期食管鳞癌临床病理特征的关系

目的:探讨术前D-二聚体(D-dimer)和血小板(PLT)计数与局部晚期食管鳞状细胞癌(esophageal squamous cell carcinoma,ESCC)临床病理特征的相关性及预后价值。方法:测定99例局部晚期ESCC患者(食管癌组)及30例健康体检者(对照组)血浆D-dimer和PLT水平并进行比较,并分析其与ESCC临床病理因素之间关系及预后价值。结果:食管癌组血浆D-dimer、PLT水平高于健康对照组(P0.05)。食管癌组D-dimer与淋巴结转移、TNM分期有关,而PLT与ESCC临床病理特征无关。单因素分析提示D-dimer、淋巴结转移、TNM分期与无疾病进展时间(disease free survival,DFS)和总体生存时间(overall survival,OS)相关,而多因素分析仅提示D-dimer是局部晚期ESCC患者的独立危险预后因素。结论:D-dimer增高与淋巴结转移和TNM分期有关,D-dimer可作为评判局部晚期ESCC预后的独立指标,为ESCC个体化治疗提供参考价值。     

Establishment and experimental validation of an immune miRNA signature for assessing prognosis and immune landscape of patients with colorectal cancer

As essential regulators of gene expression, miRNAs are engaged in the initiation and progression of colorectal cancer (CRC), including antitumour immune response. In this study, we proposed an integrated algorithm, ImmuMiRNA, for identifying miRNA modulators of immune-associated pathways. Based on these immune-associated miRNAs, we applied the LASSO algorithm to develop a reliable and individualized signature for evaluating overall survival (OS) and inflammatory landscape of CRC patients. An external public data set and qRT-PCR data from 40 samples were further utilized to validate this signature. As a result, an immune-associated miRNA prognostic signature (IAMIPS) consisting of three miRNAs (miR-194-3P, miR-216a-5p and miR-3677-3p) was established and validated. Patients in the high-risk group possessed worse OS. After stratification for clinical factors, the signature remained a powerful independent predictor for OS. IAMIPS displayed much better accuracy than the traditional clinical stage in assessing the prognosis of CRC. Further analysis revealed that patients in the high-risk group were characterized by inflammatory response, abundance immune cell infiltration, and higher immune checkpoint profiles and tumour mutation burden (TMB). In conclusion, the IAMIPS is highly predictive of OS in patients with CRC, which may serve as a powerful prognostic tool to further optimize immunotherapies for cancer.     

An estrogen receptor (ER)‐related signature in predicting prognosis of ER‐positive breast cancer following endocrine treatment

Quite a few estrogen receptor (ER)‐positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER‐related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER‐related gene signature that can predict the prognosis of ER‐positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER‐related signature was developed to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER‐related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER‐related prognostic signature is a promising method for predicting the prognosis of ER‐positive breast cancer patients receiving endocrine therapy.     

MicroRNA expression profile in serum reveals novel diagnostic biomarkers for endometrial cancer

Purpose: Circulating microRNAs (miRNAs) prove to be promising diagnostic biomarkers for various cancers, including endometrial cancer (EC). The present study aims to identify serum microRNAs that can serve as potential biomarkers for EC diagnosis.Patients and methods: A total of 92 EC and 102 normal control (NC) serum samples were analyzed using quantitative real-time polymerase chain reaction (qRT-PCR) in this four-phase experiment. The logistic regression method was used to construct a diagnostic model based on the differentially expressed miRNAs in serum. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value. To further validate the diagnostic capacity of the identified signature, the 6-miRNA marker was compared with previously reported biomarkers and verified in three public datasets. In addition, the expression characteristics of the identified miRNAs were further explored in tissue and serum exosomes samples.Results: Six miRNAs (miR-143-3p, miR-195-5p, miR-20b-5p, miR-204-5p, miR-423-3p, and miR-484) were significantly overexpressed in the serum of EC compared with NCs. Areas under the ROC of the 6-miRNA signatures were 0.748, 0.833, and 0.967 for the training, testing, and the external validation phases, respectively. The identified signature has a very stable diagnostic performance in the large cohorts of three public datasets. Compared with previously identified miRNA biomarkers, the 6-miRNA signature in the present study has superior performance in diagnosing EC. Moreover, the expression of miR-143-3p and miR-195-5p in tissues and the expression of miR-20b-5p in serum exosomes were consistent with those in serum.Conclusions: We established a 6-miRNA signature in serum and they could function as potential non-invasive biomarker for EC diagnosis.     

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.     

Identification of a 4-miRNA signature as a potential prognostic biomarker for pancreatic adenocarcinoma

An microRNA (miRNA) signature to predict the clinical outcome of pancreatic adenocarcinoma (PAAD) is still lacking. In the current study, we aimed at identifying and evaluating a prognostic miRNA signature for patients with PAAD. The miRNA expression profile and the clinical information regarding patients with PAAD were recruited from The Cancer Genome Atlas database. Differentially expressed miRNAs were identified between normal and tumor samples. By means of survival analysis, a 4-miRNA signature for predicting patients' with PAAD overall survival (OS) was constructed. Receiver operating characteristic (ROC) analysis was applied to determine the efficiency of survival prediction. Furthermore, the biological function of the predicted miRNAs was evaluated using a bioinformatics approach. Four (hsa-mir-126, hsa-mir-3613, hsa-mir-424, and hsa-mir-4772) out of 17 differentially expressed miRNAs were associated to the OS of patients with PAAD. Moreover, the area under the curve (AUC) of the constructed 4-miRNA signature associated to patients' with PAAD 2-year survival was 0.789. The multivariate Cox's proportional hazards regression model suggested that this 4-miRNA signature was an independent prognostic factor of other clinical parameters in patients with PAAD. Further pathway enrichment analyses revealed that the miRNAs in the 4-miRNA signature might regulate genes that affect focal adhesion, Wnt signaling pathway, and PI3K-Akt signaling pathway. Thus, these findings indicated that the 4-miRNA signature might be an effective independent prognostic biomarker in the prediction of PAAD patients' survival.     

Circulating microRNAs as a Fingerprint for Liver Cirrhosis

Background

Sensitive and specific detection of liver cirrhosis is an urgent need for optimal individualized management of disease activity. Substantial studies have identified circulation miRNAs as biomarkers for diverse diseases including chronic liver diseases. In this study, we investigated the plasma miRNA signature to serve as a potential diagnostic biomarker for silent liver cirrhosis.

Methods

A genome-wide miRNA microarray was first performed in 80 plasma specimens. Six candidate miRNAs were selected and then trained in CHB-related cirrhosis and controls by qPCR. A classifier, miR-106b and miR-181b, was validated finally in two independent cohorts including CHB-related silent cirrhosis and controls, as well as non−CHB-related cirrhosis and controls as validation sets, respectively.

Results

A profile of 2 miRNAs (miR-106b and miR-181b) was identified as liver cirrhosis biomarkers irrespective of etiology. The classifier constructed by the two miRNAs provided a high diagnostic accuracy for cirrhosis (AUC = 0.882 for CHB-related cirrhosis in the training set, 0.774 for CHB-related silent cirrhosis in one validation set, and 0.915 for non−CHB-related cirrhosis in another validation set).

Conclusion

Our study demonstrated that the combined detection of miR-106b and miR-181b has a considerable clinical value to diagnose patients with liver cirrhosis, especially those at early stage.     

Novel gene signatures for prognosis prediction in ovarian cancer

Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.     

Serum DSG2 as a potential biomarker for diagnosis of esophageal squamous cell carcinoma and esophagogastric junction adenocarcinoma

Background: Exploration of serum biomarkers for early detection of upper gastrointestinal cancer is required. Here, we aimed to evaluate the diagnostic potential of serum desmoglein-2 (DSG2) in patients with esophageal squamous cell carcinoma (ESCC) and esophagogastric junction adenocarcinoma (EJA).Methods: Serum DSG2 levels were measured by enzyme-linked immunosorbent assay (ELISA) in 459 participants including 151 patients with ESCC, 96 with EJA, and 212 healthy controls. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic accuracy.Results: Levels of serum DSG2 were significantly higher in patients with ESCC and EJA than those in healthy controls (P<0.001). Detection of serum DSG2 demonstrated an area under the ROC curve (AUC) value of 0.724, sensitivity of 38.1%, and specificity of 84.8% for the diagnosis of ESCC in the training cohort, and AUC 0.736, sensitivity 58.2%, and specificity 84.7% in the validation cohort. For diagnosis of EJA, measurement of DSG2 provided a sensitivity of 29.2%, a specificity of 90.2%, and AUC of 0.698. Similar results were observed for the diagnosis of early-stage ESCC (AUC 0.715 and 0.722, sensitivity 36.3 and 50%, and specificity 84.8 and 84.7%, for training and validation cohorts, respectively) and early-stage EJA (AUC 0.704, sensitivity 44.4%, and specificity 86.9%). Analysis of clinical data indicated that DSG2 levels were significantly associated with patient age and histological grade in ESCC (P<0.05).Conclusion: Serum DSG2 may be a diagnostic biomarker for ESCC and EJA.