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Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma.

ABSTRACT: Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including "pathways of basal cell carcinoma", "glycerolipid metabolism", etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson's analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

SUBMITTER: Liu YJ

PROVIDER: S-EPMC8674813 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Colorectal cancer (CRC) is a commonly occurring tumour with poor prognosis. Autophagy-related long non-coding RNAs (lncRNAs) have received much attention as biomarkers for cancer prognosis and diagnosis. However, few studies have focused on their prognostic predictive value specifically in CRC. This research aimed to construct a robust autophagy-related lncRNA prognostic signature for CRC. Autophagy-related lncRNAs from The Cancer Genome Atlas database were screened using univariate Cox, LASSO, and multivariate Cox regression analyses, and the resulting key lncRNAs were used to establish a prognostic risk score model. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to detect the expression of several lncRNAs in cancer tissues from CRC patients and in normal tissues adjacent to the cancer tissues. A prognostic signature comprising lncRNAs AC125603.2, LINC00909, AC016876.1, MIR210HG, AC009237.14, and LINC01063 was identified in patients with CRC. A graphical nomogram based on the autophagy-related lncRNA signature was developed to predict CRC patients' 1-, 3-, and 5-year survival. Overall survival in patients with low risk scores was significantly better than in those with high risk scores (P < 0.0001); a similar result was obtained in an internal validation sample. The nomogram was shown to be suitable for clinical use and gave correct predictions. The 1- and 3-year values of the area under the receiver operating characteristic curve were 0.797 and 0.771 in the model sample, and 0.656 and 0.642 in the internal validation sample, respectively. The C-index values for the verification samples and training samples were 0.756 (95% CI = 0.668-0.762) and 0.715 (95% CI = 0.683-0.829), respectively. Gene set enrichment analysis showed that the six autophagy-related lncRNAs were greatly enriched in CRC-related signalling pathways, including p53 and VEGF signalling. The qRT-PCR results showed that the expression of lncRNAs in CRC was higher than that in adjacent tissues, consistent with the expression trends of lncRNAs in the CRC data set. In summary, we established a signature of six autophagy-related lncRNAs that could effectively guide clinical prediction of prognosis in patients with CRC. This lncRNA signature has significant clinical implications for improving the prediction of outcomes and, with further prospective validation, could be used to guide tailored therapy for CRC patients.

Project description:Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson's correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

Project description:AbstractReliable biomarkers are of great significance for the treatment and diagnosis of hepatocellular carcinoma (HCC). This study identified potential prognostic epithelial-mesenchymal transition related lncRNAs (ERLs) by the cancer genome atlas (TCGA) database and bioinformatics.The differential expression of long noncoding RNA (lncRNA) was obtained by analyzing the lncRNA data of 370 HCC samples in TCGA. Then, Pearson correlation analysis was carried out with EMT related genes (ERGs) from molecular signatures database. Combined with the univariate Cox expression analysis of the total survival rate of hepatocellular carcinoma (HCC) patients, the prognostic ERLs were obtained. Then use "step" function to select the optimal combination of constructing multivariate Cox expression model. The expression levels of ERLs in HCC samples were verified by real-time quantitative polymerase chain reaction.Finally, we identified 5 prognostic ERLs (AC023157.3, AC099850.3, AL031985.3, AL365203.2, CYTOR). The model showed that these prognostic markers were reliable independent predictors of risk factors (P value <.0001, hazard ratio [HR] = 2.400, 95% confidence interval [CI] = 1.667-3.454 for OS). In the time-dependent receiver operating characteristic analysis, this prognostic marker is a good predictor of HCC survival (area under the curve of 1 year, 2 years, 3 years, and 5 years are 0.754, 0.720, 0.704, and 0.662 respectively). We analyzed the correlation of clinical characteristics of these prognostic markers, and the results show that this prognostic marker is an independent factor that can predict the prognosis of HCC more accurately. In addition, by matching with the Molecular Signatures Database, we obtained 18 ERLs, and then constructed the HCC prognosis model and clinical feature correlation analysis using 5 prognostic ERLs. The results show that these prognostic markers have reliable independent predictive value. Bioinformatics analysis showed that these prognostic markers were involved in the regulation of EMT and related functions of tumor occurrence and migration.Five prognostic types of ERLs identified in this study can be used as potential biomarkers to predict the prognosis of HCC.

Project description:BackgroundThe association between long noncoding RNAs (lncRNAs) and spontaneous regression of neuroblastoma (NB) has rarely been investigated and remains unknown.ObjectiveTo identify prognostic lncRNAs involved in the spontaneous regression of NB.MethodsDifferential expression analyses were performed between those samples with an outcome of death in stage 4 NB group and those samples with an outcome of survival in stage 4S NB group in two independent public datasets, respectively. Univariate Cox proportional hazard regression survival analysis was performed in each of the entire cohort to identify those lncRNAs significantly associated with overall survival (OS). Those lncRNAs independently associated with OS were then identified by multivariate Cox survival analysis and used to construct an lncRNA risk score.ResultsA total of 20 differentially expressed and survival-related lncRNAs were identified sharing between the two independent cohorts. The expression of each of these 20 lncRNAs was significantly correlated with the expression of NTRK1, which is a well-known factor involved in NB spontaneous regression. Four lncRNAs (LNC00839, FIRRE, LOC283177, and LOC101928100) were identified to be significantly associated with survival independent with each other and a four-lncRNA signature risk score was constructed. Patients with high lncRNA signature risk score had a significantly poorer OS and event-free survival than those with low lncRNA signature risk score. The four-lncRNA signature has a good performance in predicting survival independent with MYCN amplification (nonamplified vs amplified), age status (<18 months vs ≥18 months), risk status (low risk vs high risk), and International Neuroblastoma Staging System (INSS) stage (INSS 1/2/3/4S vs INSS 4).ConclusionsWe identified 20 survival-related lncRNAs that might be associated with the spontaneous regression of NB and developed a four-lncRNA signature risk score. The four-lncRNA signature is an independent prognostic factor for survival of NB patients.

Project description:IntroductionColorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor-node-metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment.MethodsCRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot.ResultsA prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased.ConclusionThe new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

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Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma.

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