Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.     

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.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

Background

Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer.

Methodology/Principal Findings

Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008).

Conclusions/Significance

The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.     

Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director''s Challenge Consortium. Four independent validation datasets, including {"type":"entrez-geo","attrs":{"text":"GSE5843","term_id":"5843"}}GSE5843, {"type":"entrez-geo","attrs":{"text":"GSE8894","term_id":"8894"}}GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.     

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.     

Identification of key lncRNAs in the carcinogenesis and progression of colon adenocarcinoma by co-expression network analysis

Colon adenocarcinoma (COAD) is one of the most common cancers, and its carcinogenesis and progression is influenced by multiple long non-coding RNAs (lncRNA), especially through the miRNA sponge effect. In this study, more than 4000 lncRNAs were re-annotated from the microarray datasets through probe sequence mapping to obtain reliable lncRNA expression profiles. As a systems biology method for describing the correlation patterns among genes across microarray samples, weighted gene co-expression network analysis was conducted to identify lncRNA modules associated with the five stepwise stages from normal colonic samples to COAD (n = 94). In the most relevant module (R² = −0.78, P = 4E-20), four hub lncRNAs were identified (CTD-2396E7.11, PCGF5, RP11-33O4.1, and RP11-164P12.5). Then, these four hub lncRNAs were validated using two other independent datasets including GSE20916 (n = 145) and GSE39582 (n = 552). The results indicated that all hub lncRNAs were significantly negatively correlated with the three-stage colonic carcinogenesis, as well as TNM stages in COAD (one-way analysis of variance P < 0.05). Kaplan-Meier survival curve showed that patients with higher expression of each hub lncRNA had a significantly higher overall survival rate and lower relapse risk (log-rank P < 0.05). In conclusion, through co-expression analysis, we identified and validated four key lncRNAs in association with the carcinogenesis and progression of COAD, and these lncRNAs might have important clinical implications for improving the risk stratification, therapeutic decision and prognosis prediction in COAD patients.     

Distinct Gene Expression Signatures in Lynch Syndrome and Familial Colorectal Cancer Type X

Introduction

Heredity is estimated to cause at least 20% of colorectal cancer. The hereditary nonpolyposis colorectal cancer subset is divided into Lynch syndrome and familial colorectal cancer type X (FCCTX) based on presence of mismatch repair (MMR) gene defects.

Purpose

We addressed the gene expression signatures in colorectal cancer linked to Lynch syndrome and FCCTX with the aim to identify candidate genes and to map signaling pathways relevant in hereditary colorectal carcinogenesis.

Experimental design

The 18 k whole-genome c-DNA-mediated annealing, selection, extension, and ligation (WG-DASL) assay was applied to 123 colorectal cancers, including 39 Lynch syndrome tumors and 37 FCCTX tumors. Target genes were technically validated using real-time quantitative RT-PCR (qRT-PCR) and the expression signature was validated in independent datasets.

Results

Colorectal cancers linked to Lynch syndrome and FCCTX showed distinct gene expression profiles, which by significance analysis of microarrays (SAM) differed by 2188 genes. Functional pathways involved were related to G-protein coupled receptor signaling, oxidative phosphorylation, and cell cycle function and mitosis. qRT-PCR verified altered expression of the selected genes NDUFA9, AXIN2, MYC, DNA2 and H2AFZ. Application of the 2188-gene signature to independent datasets showed strong correlation to MMR status.

Conclusion

Distinct genetic profiles and deregulation of different canonical pathways apply to Lynch syndrome and FCCTX and key targets herein may be relevant to pursue for refined diagnostic and therapeutic strategies in hereditary colorectal cancer.     

Molecular Characteristics and Metastasis Predictor Genes of Triple-Negative Breast Cancer: A Clinical Study of Triple-Negative Breast Carcinomas

Background

Triple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible.

Methodology/Principal Findings

Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04–5.06, Cox P = 0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-β signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer.

Conclusions/Significance

Gene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.     

Prognostic Significance of the Metabolic Marker Hexokinase-2 in Various Solid Tumors: A Meta-Analysis

ObjectiveRecently, numerous studies have reported that hexokinase-2 (HK2) is aberrantly expressed in cancer, indicating that HK2 plays a pivotal role in the development and progression of cancer. However, its prognostic significance in solid tumor remains unclear. Accordingly, we performed a meta-analysis to assess the prognostic value of HK2 in solid tumor.MethodsEligible studies were identified using PubMed, Embase, and Web of Science databases. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) or progression-free survival (PFS)/disease-free survival (DFS)/relapse-free survival (RFS) were estimated with random effects or fixed effects models, respectively. Subgroup analysis was also performed according to patients’ ethnicities, tumor types, detection methods, and analysis types.ResultsData from 21 included studies with 2532 patients were summarized. HK2 overexpression was significantly associated with worse OS (pooled HR = 1.90, 95% CI = 1.51–2.38, p < 0.001) and PFS (pooled HR = 2.91, 95% CI = 2.02–4.22, p < 0.001) in solid tumor. As to a specific form of cancer, the negative effect of HK2 on OS was observed in hepatocellular carcinoma (pooled HR = 2.06, 95% CI = 1.67–2.54, p < 0.001), gastric cancer (pooled HR = 1.72, 95% CI = 1.09–2.71, p = 0.020), colorectal cancer (pooled HR = 2.89, 95% CI = 1.62–5.16, p < 0.001), but not in pancreatic cancer (pooled HR = 1.13, 95% CI = 0.28–4.66, p = 0.864). No publication bias was found in the included studies for OS (Begg’s test, p = 0.325; Egger’s test, p = 0.441).ConclusionIn this meta-analysis, we identified that elevated HK2 expression was significantly associated with shorter OS and PFS in patients with solid tumor, but the association varies according to cancer type.     

Clinical and Prognostic Significance of Preoperative Plasma Fibrinogen Levels in Patients with Operable Breast Cancer

Purpose

Elevated plasma fibrinogen levels are associated with tumor progression and poor outcomes in different cancer patients. The objective of this study was to investigate the clinical and prognostic value of preoperative plasma fibrinogen levels in patients with operable breast cancer.

Methods

Two hundred and twenty-three patients diagnosed with breast cancer were retrospectively evaluated in this study. Plasma fibrinogen levels were examined before treatment and analyzed along with patient clinicopathological parameters, disease-free survival (DFS) and overall survival(OS). Both univariate and multivariate analyses were performed to identify the clinicopathological parameters associated with DFS and OS.

Results

Elevated preoperative plasma fibrinogen levels were directly associated with age of diagnose (≤47 vs. >47, p<0.001), menopause (yes vs. no, p<0.001), tumor size (T1&T2 vs.T3&T4, p = 0.033), tumor stage (Ⅰvs.Ⅱvs.Ⅲ, p = 0.034) and lymph node involvement (N = 0 vs. 1≤N≤3 vs. N≥4, p<0.001), but not with histological grade, molecular type and other Immunohistochemical parameters(ER, PR, HER2 and Ki-67). In a univariate survival analysis, tumor stage, tumor size, lymph node involvement (p<0.001/ p<0.001)and plasma fibrinogen (p<0.001/ p<0.001) levels were associated with disease-free and overall survival, but just lymph nodes involvement (p<0.001, hazard ratio [HR] = 2.9, 95% confidence interval [CI] = 1.6–5.3/ p = 0.006, HR = 3.2, 95% CI = 1.4–7.3) and plasma fibrinogen levels (p = 0.006, HR = 3.4, 95% CI = 1.4–8.3/ p = 0.002, HR = 10.1, 95% CI = 2.3–44.6) were associated with disease-free and overall survival in a multivariate survival analysis, respectively.

Conclusions

This study demonstrates that elevated preoperative plasma fibrinogen levels are associated with breast cancer progression and are independently associated with a poor prognosis in patients with operable breast cancer.     

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.     

Identification of a prognostic gene signature based on an immunogenomic landscape analysis of bladder cancer

Cancer immune plays a critical role in cancer progression. Tumour immunology and immunotherapy are one of the exciting areas in bladder cancer research. In this study, we aimed to develop an immune‐related gene signature to improve the prognostic prediction of bladder cancer. Firstly, we identified 392 differentially expressed immune‐related genes (IRGs) based on TCGA and ImmPort databases. Functional enrichment analysis revealed that these genes were enriched in inflammatory and immune‐related pathways, including in ‘regulation of signaling receptor activity’, ‘cytokine‐cytokine receptor interaction’ and ‘GPCR ligand binding’. Then, we separated all samples in TCGA data set into the training cohort and the testing cohort in a ratio of 3:1 randomly. Data set {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507 was set as the validation cohort. We constructed a prognostic six‐IRG signature with LASSO Cox regression in the training cohort, including AHNAK, OAS1, APOBEC3H, SCG2, CTSE and KIR2DS4. Six IRGs reflected the microenvironment of bladder cancer, especially immune cell infiltration. The prognostic value of six‐IRG signature was further validated in the testing cohort and the validation cohort. The results of multivariable Cox regression and subgroup analysis revealed that six‐IRG signature was a clinically independent prognostic factor for bladder cancer patients. Further, we constructed a nomogram based on six‐IRG signature and other clinicopathological risk factors, and it performed well in predict patients'' survival. Finally, we found six‐IRG signature showed significant difference in different molecular subtypes of bladder cancer. In conclusions, our research provided a novel immune‐related gene signature to estimate prognosis for patients'' survival with bladder cancer.     

The diagnostic and prognostic values of microRNA-196a in cancer

MicroRNA-196a (miR-196a) was previously reported to be up-regulated in cancers, and it has the diagnostic and prognostic values in cancers. Whereas, the conclusion was still unclear according to the published data. To assess such roles of miR-196a in cancers, the present study was conducted based on published data and online cancer-related databases. To identify the relevant published data, we searched articles in databases and then the relevant data were extracted to evaluate the correlation between miR-196a expression and diagnosis, prognosis for cancer patients. The pooled results showed that miR-196a was a valuable diagnostic biomarker in cancer (area under curve (AUC) = 0.87, 95% CI: 0.84–0.90; sensitivity (SEN) = 0.73, 95% CI: 0.64–0.81; specificity (SPE) = 0.90, 95% CI: 0.81–0.95), which was consistent with the data from databases (breast cancer: miR-196a-3p: AUC = 0.77, 95% CI: 0.74–0.79; miR-196a-5p: AUC = 0.71, 95% CI: 0.66–0.75; pancreatic cancer: miR-196a-3p: AUC = 0.80, 95% CI: 0.73–0.87; miR-196a-5p: AUC = 0.61, 95% CI: 0.51–0.71). In addition, the pooled result revealed that elevated miR-196a expression in tumor tissues (HR = 2.54, 95% CI: 1.79–3.61, P_{Heterogeneity}=0.000, I² = 75.8%) or serum/plasma (HR = 4.06, 95% CI: 2.67–6.18, P_{Heterogeneity}=0.668, I² = 0%) of patients was an unfavorable survival biomarker, which was consistent with the data from databases (adrenocortical carcinoma: HR = 5.70; esophageal carcinoma: HR = 1.93; brain lower grade glioma: HR = 2.91; {"type":"entrez-geo","attrs":{"text":"GSE40267","term_id":"40267"}}GSE40267: HR = 2.47, 95% CI: 1.2–5.07; TCGA: HR = 1.82, 95% CI: 1.21–2.74; {"type":"entrez-geo","attrs":{"text":"GSE19783","term_id":"19783"}}GSE19783: HR = 4.24, 95% CI: 1–18.06). In short, our results demonstrated that miR-196a in tumor tissue or serum/plasma could be used as a prognostic and diagnostic values for cancers.     

Identification of CEACAM5 as a Biomarker for Prewarning and Prognosis in Gastric Cancer

MGd1, a monoclonal antibody raised against gastric cancer cells, possesses a high degree of specificity for gastric cancer (GC). Here we identified that the antigen of MGd1 is CEACAM5, and used MGd1 to investigate the expression of CEACAM5 in non-GC and GC tissues (N=643), as a biomarker for prewarning and prognosis. The expression of CEACAM5 was detected by immunohistochemistry in numerous tissues; its clinicopathological correlation was statistically analyzed. CEACAM5 expression was increased progressively from normal gastric mucosa to chronic atrophic gastritis, intestinal metaplasia, dysplasia and finally to GC (p<0.05). In gastric precancerous lesions (intestinal metaplasia and dysplasia), CEACAM5-positive patients had a higher risk of developing GC as compared with CEACAM5-negative patients (OR = 12.68, p<0.001). Besides, CEACAM5 was found positively correlated with invasion depth of gastric adenocarcinoma (p<0.001). In survival analysis, CEACAM5 was demonstrated to be an independent prognostic predictor for patients with GC of clinical stage IIIA/IV (p=0.033). Our results demonstrate that CEACAM5 is a promising biomarker for GC prewarning and prognostic evaluation.     

Prognostic Value of Overexpressed p16INK4a in Vulvar Cancer: A Meta-Analysis

Objective

This study aimed to examine the prognostic value of overexpressed p16^INK4a in vulvar cancer. Although the tumor suppressor p16^INK4a has been shown to be of prognostic value in a wide variety of cancers and precancerous lesions, its role in the vulvar cancer is still unclear.

Methods

All publications in English language on the association between p16^INK4a and clinicopathological features of vulvar cancer were searched from Pubmed, Embase, and Web of Science, and those in Chinese language were identified manually and online from the China National Knowledge Infrastructure. Strict inclusion and exclusion criteria were followed. Odds ratios(ORs) or risk ratios(RRs) with 95% confidence intervals(CIs) were pooled to assess the strength of association. Publication bias was estimated using funnel plots and the Egger’s regression test.

Results

A total of 17 studies with 2309 patients were included. The p16^INK4a overexpression was found to correlate significantly with the lower International Federation of Gynecology and Obstetrics stage(I+II vs III+IV; OR = 0.60,95%CI:0.41–0.86,P = 0.006),negative lymph node metastasis(negative vs positive; OR = 0.61,95%CI:0.39–0.95,P = 0.029),patient’s age<55(OR = 0.54,95%CI:0.31–0.96,P = 0.034),human papillomavirus–positive status(OR = 0.01,95%CI:0.00–0.11,P<0.001),and higher overall survival(RR = 0.53,95%CI = 0.35–0.80,P = 0.003).

Conclusion

The p16^INK4a might be associated with a higher survival and indicates better prognosis of vulvar cancer.     

Identification of IMPA2 as the hub gene associated with colorectal cancer and liver metastasis by integrated bioinformatics analysis

Background and ObjectivesColorectal cancer (CRC) is one of the most common malignant tumors worldwide with high incidence and mortality rate, while colorectal liver metastasis (CRLM) is one of the major causes of cancer-related deaths. Therefore, the present study aims to identify the hub gene associated with CRC carcinogenesis and liver metastasis, and then explore its diagnostic and prognostic value as well as the potential regulation mechanism.MethodsThe overlapping differential co-expression genes among CRC, CRLM, and normal tissues were explored on the GSE49355 and GSE81582 datasets from the Gene Expression Omnibus (GEO) database by integrated bioinformatics analysis. Then, the hub prognostic genes were selected from the overlapping genes by univariate Cox proportional hazard analysis and online database Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Subsequently, the clinical value of the hub genes was evaluated in the TCGA and GSE39582 cohorts. Finally, the underlying mechanisms of the hub gene regulating CRC carcinogenesis and metastasis were explored by Gene function annotation and DNA methylation analysis.ResultsInositol mono-phosphatase 2 (IMPA2) was identified as the hub gene associated with CRC carcinogenesis and liver metastasis. IMPA2 had an excellent diagnostic efficiency, and its expression was significantly decreased in CRC and liver metastasis samples, being positively correlated with poor prognosis. Moreover, its low expression was associated with AJCC stage III+IV, T4, N1+2, and M1. In addition, our results revealed that the potential mechanisms used by IMPA2 to mediate CRC carcinogenesis and metastasis could be associated with lipid metabolism and epithelial mesenchymal transition (EMT). Finally, IMPA2 expression could be regulated by DNA methylation.ConclusionsIMPA2 was identified and reported for the first time as a hub gene biomarker in the diagnosis and prognosis of CRC, which could regulate CRC carcinogenesis and liver metastasis through the regulation of lipid metabolism, EMT, and DNA methylation.     

Prognostic Role of microRNA-21 in Colorectal Cancer: a Meta-Analysis

Background

To date, many studies have shown that microRNAs (miRNA) exhibit altered expression in various cancers and may play an important role as prognostic biomarker of cancers. The present meta-analysis summarizes the recent advances in the use of microRNA-21 (miR-21) in the assessment of colorectal cancer and analyzes the prognostic role of miR-21 for survival outcome.

Methodology/Principal Findings

The present meta-analysis was performed by searching PubMed through multiple search strategies. Data were extracted from studies comparing overall survival (OS) in patients with colorectal cancer who showed higher expression of miR-21 than similar patients. Pooled hazard ratios (HRs) of miR-21 for survival and 95% confidence intervals (CI) were calculated. Seven studies with a total of 1174 patients were included this meta-analysis. For overall survival (OS), the pooled hazard ratio (HR) of higher miR-21 expression in colorectal cancer was 1.76 (95% CI: 1.34–2.32, P=0.000). After elimination of heterogeneity, the pooled HR was 2.32 (95% CI: 1.82–2.97, P=0.000), which was found to significantly predict poorer survival. The subgroup analysis suggested that elevated miR-21 level and patients’ survival correlated with III/IV stage (HR=5.35, 95% CI: 3.73–7.66).

Conclusions/Significance

The present findings suggest that high expression of miR-21 might predict poor prognosis in patients with colorectal cancer.