Long non-coding RNA profile study identifies a metabolism-related signature for colorectal cancer.

Heterogeneity in colorectal cancer (CRC) patients provides novel strategies in clinical decision-making. Identifying distinctive subgroups in patients can improve the screening of CRC and reduce the cost of tests. Metabolism-related long non-coding RNA (lncRNA) can help detection of tumorigenesis and development for CRC patients. RNA sequencing and clinical data of CRC patients which extracted and integrated from public databases including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were set as training cohort and validation cohort. Metabolism-related genes were acquired from Kyoto Encyclopedia of Genes and Genomes (KEGG) and the metabolism-related lncRNAs were filtered using correlation analysis. The risk score was calculated based on lncRNAs with prognostic value and verified through survival curve, receiver operating characteristic (ROC) curve and risk curve. Prognostic factors of CRC patients were also analyzed. Nomogram was constructed based on the results of cox regression analyses. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA). The training cohort and the validation cohort enrolled 432 and 547 CRC patients respectively. A total of 23 metabolism-related lncRNAs with prognostic value were screened out and 10 of which were significantly differentially expressed between tumour and normal tissues. Finally, 8 lncRNAs were used to establish a risk score (DICER1-AS1, PCAT6, GAS5, PRR7-AS1, MCM3AP-AS1, GAS6-AS1, LINC01082 and ADIRF-AS1). Patients were divided into high-risk and low-risk groups according to the median of risk scores in training cohort and the survival curves indicated that the survival prognosis was significantly different. The area under curve (AUC) of the ROC curve in two cohorts were both greater than 0.6. The age, tumour stage and risk score were selected as independent factors and used to construct a nomogram to predict CRC patients' survival rate with the c-index of 0.806. The ssGSEA indicated that the risk score was associated with immune cells and functions. Our systematic study established a metabolism-related lncRNA signature to predict outcomes of CRC patients which may contribute to individual prevention and treatment.

Lu Y ，Wang W ，Liu Z ，Ma J ，Zhou X ，Fu W ... -  《-》

Identification and Validation of Six Autophagy-related Long Non-coding RNAs as Prognostic Signature in Colorectal Cancer.

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.

Cheng L ，Han T ，Zhang Z ，Yi P ，Zhang C ，Zhang S ，Peng W ... -  《International Journal of Medical Sciences》

Identification of a Prognostic Colorectal Cancer Model Including LncRNA FOXP4-AS1 and LncRNA BBOX1-AS1 Based on Bioinformatics Analysis.

Shi ZL ，Zhou GQ ，Guo J ，Yang XL ，Yu C ，Shen CL ，Zhu XG ... -  《-》

Discovery of a novel six-long non-coding RNA signature predicting survival of colorectal cancer patients.

Colorectal cancer (CRC) is one of the most leading causes of cancer deaths worldwide. In the study, we aimed to identify key long non-coding RNAs (lncRNAs) significantly associated with prognosis of CRC and develop an expression-based lncRNA signature to provide survival risk prediction for CRC patients. LncRNA expression profiles and clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Six hundred and eighteen differentially expressed lncRNAs were identified between CRC and normal tissues. After univariate and multivariate Cox regression analysis for these differentially expressed lncRNAs and overall survival of CRC patients, six predictive lncRNAs (RP1-170O19.17, RP11-785D18.3, RP11-798K3.2, XXbac-B476C20.9, RP11-481J13.1, and RP11-167H9.4) were finally screened out to construct a six-lncRNA signature, based on which patients in the training dataset were divided into the high-risk and low-risk group with significantly different overall survival. ROC curve analysis demonstrated competitive performance of the six-lncRNA signature. The prognostic power of the six-lncRNA signature was successfully validated in the testing and entire dataset. Multivariate Cox regression analysis and stratification analysis further suggested that the six-lncRNA signature was independent of other conventional clinical variables for survival prediction in CRC patients. Functional enrichment analysis revealed the possible roles of these predictive lncRNAs in some cancer-related biological processes and pathways. Our study demonstrated the promising potential of the novel six-lncRNA signature as an independent biomarker for survival prediction of CRC patients.

Fan Q ，Liu B 《-》

Bioinformatics profiling utilized a nine immune-related long noncoding RNA signature as a prognostic target for pancreatic cancer.

To identify an immune-related long noncoding RNA (lncRNA) signature with potential prognostic value for patients with pancreatic cancer. Pancreatic cancer samples with available clinical information and whole genomic mRNA expression data obtained from The Cancer Genome Atlas (TCGA) were enrolled in our research. The immune score of each sample was calculated according to the expression level of immune-related genes and used to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high- and low-risk groups were separated on the basis of the median risk score. The prediction reliability was further evaluated in the validation set and combination set. Both gene set enrichment analysis (GSEA) and principal component analysis (PCA) were performed for functional annotation, and the microenvironment cell population record was applied to evaluate the immune composition and purity of the tumor. A cohort of 176 samples was included in this study. A total of 163 immune-related lncRNAs were collected according to Pearson correlation analyses between immune score and lncRNA expression |R| > 0.5, P < 0.01). Nine immune-related lncRNAs (AL138966.2, AL133520.1, AC142472.1, AC127024.5, AC116913.1, AC083880.1, AC124016.1, AC008443.5, and AC092171.5) with the most significant prognostic values (P < 0.01) were identified. In the training set, it was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group (P < 0.001); meanwhile, similar results were found in the validation set, combination set and various stratified sets (P < 0.05, P < 0.001, P < 0.05, respectively). Moreover, the signature was identified as an independent prognostic factor and significantly associated with the OS of pancreatic cancer. The area under curve (AUC) of the receiver operating characteristic curve (ROC curve) for the nine lncRNA signature in predicting the 2-year survival rate was 0.703. In addition, the low-risk and high-risk groups displayed different distributed patterns in PCA and different immune statuses in the GSEA. The signature indicated decreased purity of the tumor by implying a lower proportion of cancer cells along with an increasing enrichment of fibroblasts, myeloid dendritic cells, and monocytic lineage cells. Our research suggests that the immune-related lncRNA signature possesses latent prognostic value for patients with pancreatic cancer and may provide new information for immunological research and treatment in pancreatic cancer.

Wei C ，Liang Q ，Li X ，Li H ，Liu Y ，Huang X ，Chen X ，Guo Y ，Li J ... -  《-》