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.     

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.     

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.     

Prediction of head and neck squamous cell carcinoma survival based on the expression of 15 lncRNAs

Recent evidence suggests that long noncoding RNAs (lncRNAs) are essential regulators of many cancer-related processes, including cancer cell proliferation, invasion, and migration. There is thus a reason to believe that the detection of lncRNAs may be useful as a diagnostic and prognostic strategy for cancer detection, however, at present no effective genome-wide tests are available for clinical use, constraining the use of such a strategy. In this study, we performed a comprehensive assessment of lncRNAs expressed in samples in the head and neck squamous cell carcinoma (HNSCC) cohort available in The Cancer Genome Atlas database. A risk score (RS) model was constructed based on the expression data of these 15 lncRNAs in the validation data set of HNSCC patients and was subsequently validated in validation data set and the entire data set. We were able to stratify patients into high- and low-risk categories, using our lncRNA expression panel to determine an RS, with significant differences in overall survival (OS) between these two groups in our test set (median survival, 1.863 vs. 5.484 years; log-rank test, p < 0.001). We were able to confirm the predictive value of our 15-lncRNA signature using both a validation data set and a full data set, finding our signature to be reproducible and effective as a means of predicting HNSCC patient OS. Through the multivariate Cox regression and stratified analyses, we were further able to confirm that the predictive value of this RS was independent of other predictive factors such as clinicopathological parameters. The Gene set enrichment analysis revealed potential functional roles for these 15 lncRNAs in tumor progression. Our findings indicate that an RS established based on a panel of lncRNA expression signatures can effectively predict OS and facilitate patient stratification in HNSCC.     

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.     

A ten-microRNA expression signature predicts survival in glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n=222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.     

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

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

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.     

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (−0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.     

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.     

A robust two-gene signature for glioblastoma survival prediction

Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median: 1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival.     

A novel microRNA-based signature predicts prognosis among nasopharyngeal cancer patients

Nasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. miRNA expression profile and clinical information were obtained from two microarray profiling data sets from the Gene Expression Omnibus (GEO) database. miRNA signature model was constructed via univariate Cox survival analysis, multivariate Cox survival analysis, and least absolute shrinkage and selection operator Cox regression analysis. Kaplan–Meier curve, area under the curve (AUC), decision curve analysis, Box plot, and nomogram were used to evaluate the prognosis of the model to patients. 67 up-regulated and 93 down-regulated miRNAs were identified from GEO microarray data sets (P < 0.05). A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients (P  < 0.001). The AUCs for the signature were 0.74, 0.7 for the training set and external validation set. The AUC of disease free survival and distant metastasis-free survival were also high. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics (P < 0.0001). Compared with the published models, our model had a higher AUC. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma.Impact statementNasopharyngeal cancer is one of the most common malignant tumors in the head and neck. Identification of promising miRNA biomarkers might benefit a lot to the detection of nasopharyngeal carcinoma. A three-miRNA signature (has-miR-142-3p, has-miR-29c, and has-miR-30e) was obviously associated with the overall survival of nasopharyngeal carcinoma patients. The model has better clinical independence and has better clinical prediction effect when combined with clinical characteristics. Our results revealed that a three-miRNA signature was a potential novel prognostic biomarker for nasopharyngeal carcinoma.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

A prognostic eight-lncRNA expression signature in predicting recurrence of ER-positive breast cancer receiving endocrine therapy

Long noncoding RNAs (lncRNAs) have the main role in the tumorigenesis of breast cancer. In the present study, lncRNA expression profiling was collected to identify a lncRNA expression signature from the Gene Expression Omnibus database. An eight-lncRNA signature was established to predict the survival of patients with estrogen receptor (ER)-positive breast cancer receiving endocrine therapy. Patients were separated into a low-risk group and a high-risk group based on this signature. Patients in high-risk group have worse survival compared to those in low-risk group using Kaplan–Meier curve analysis with log-rank test. Receiver operating characteristic analysis suggested good diagnostic efficiency of the eight-lncRNA signature. When adjusting the clinical features, including age, grade, lymph node status, and tumor size, this signature was independently associated with the relapse-free survival. The prognostic value of the lncRNA prognostic model was then validated in validation sets. When validated in a cohort of patients treated with neoadjuvant chemotherapy and endocrine therapy, this signature demonstrated good performance as well. Besides, we have built a nomogram that integrated the conventional clinicopathological features and the eight-lncRNA-based signature. To sum up, our results indicated that the eight-lncRNA prognostic model was a reliable tool to group patients at high and low risk of disease relapse. This signature may have possible implication in prognostic evaluations of patients with ER-positive breast cancer receiving endocrine therapy.     

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.     

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

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.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.