Dataset Information

Global expression profiling of metabolic pathway-related lncRNAs in human gastric cancer and the identification of RP11-555H23.1 as a new diagnostic biomarker.

ABSTRACT: BACKGROUND:Long noncoding RNAs (lncRNAs) have roles in regulating metabolism; however, the global expression profile of metabolic pathway-associated lncRNAs in gastric cancer is unknown. The purpose of our study was to examine metabolic pathway-related lncRNAs in gastric cancer and their possible diagnostic values. METHODS:Differential expression patterns of metabolic pathway-related lncRNAs between gastric cancer and paired nontumor tissues were detected using metabolic pathway-associated lncRNA microarrays. The expression of RP11-555H23.1, one representative metabolic pathway-associated lncRNA, was validated using quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). The associations between RP11-55H23.1 expression and the clinicopathological features of gastric cancer patients were analyzed. A receiver operating characteristic (ROC) curve was further established. RESULTS:A total of 114 differentially expressed metabolic pathway-associated lncRNAs (fold change >2, P < 0.05) between cancer and nontumor tissues were found (GEO No. GSE96856). Among them, TUG1, RP11-555H23.1, RP1-257I20.13, UGP2, GCSHP3, and XLOC_000889 lncRNAs were downregulated more than sixfold in gastric cancer tissues. In contrast, RP11-605F14.2, TBC1D3P5, BC130595, LINC00475, RP11-19P22.6, BC080653, XLOC_004923, AFAP1-AS1, EPB49, and RP11-296I10.3 lncRNAs were upregulated more than sixfold in gastric cancer tissues. We further demonstrated that RP11-555H23.1 expression was significantly correlated with TNM stage (P = 0.038). The area under the ROC curve (AUC) was 0.65, and the specificity and sensitivity were 62% and 81%, respectively. CONCLUSIONS:Metabolic pathway-associated lncRNAs play an important role in the occurrence of gastric cancer, and metabolic pathway-associated lncRNAs, such as RP11-555H23.1, may represent novel biomarkers of gastric cancer.

SUBMITTER: Mo X

PROVIDER: S-EPMC6818562 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Global expression profiling of metabolic pathway-related lncRNAs in human gastric cancer and the identification of RP11-555H23.1 as a new diagnostic biomarker.

Mo Xiaoyan X Wu Yuanyuan Y Chen Li L Zhai Ming M Gao Zhengdong Z Hu Kainan K Guo Junming J

Journal of clinical laboratory analysis 20181015 2

<h4>Background</h4>Long noncoding RNAs (lncRNAs) have roles in regulating metabolism; however, the global expression profile of metabolic pathway-associated lncRNAs in gastric cancer is unknown. The purpose of our study was to examine metabolic pathway-related lncRNAs in gastric cancer and their possible diagnostic values.<h4>Methods</h4>Differential expression patterns of metabolic pathway-related lncRNAs between gastric cancer and paired nontumor tissues were detected using metabolic pathway-a ...[more]

PMID: 30320481

Similar Datasets

Project description:Background: The morbidity and mortality of gastric cancer (GC) remain high worldwide. With the advent of the Human Genome Sequencing Project, circular RNAs (circRNAs) have attracted widespread attention in cancer research due to their stable ring structure. Our aim was to identify differentially expressed circRNAs in GC and explore their potential roles in GC diagnosis, treatment, and prognostic prediction. Methods: Large-scale gene screening was performed in three pairs of GC tissues and adjacent noncancerous tissues using high-throughput sequencing. The expression of hsa_circ_0001821 was detected in 80 pairs of tissue samples by quantitative real-time PCR (qRT-PCR). Stability of the ring structure of hsa_circ_0001821 RNA was verified by exonuclease digestion assay, and its diagnostic value was evaluated by receiver operating characteristic (ROC) analysis. In addition, the location of hsa_circ_0001821 in GC cells was detected by nucleoplasm separation assay. Results: A total of 25,303 circRNAs were identified, among which 2,007 circRNAs were differentially expressed (fold change > 2.0, P < 0.05). Further validation disclosed that hsa_circ_0001821 was significantly downregulated in the 80 pairs of GC tissues and 30 whole-blood specimens obtained from the GC patients. The specificity of hsa_circ_0001821 in GC was higher than that in other solid tumors. In addition, hsa_circ_0001821 was relatively stable after RNA exonuclease digestion. Clinicopathological parameter analysis showed that hsa_circ_0001821 was negatively correlated with tumor depth (r = -0.255, P = 0.022) and lymph node metastasis (r = -0.235, P = 0.036). Area under the curve (AUC) analysis showed that the diagnostic efficiency of circulating hsa_circ_0001821 in distinguishing GC patients was higher than that in GC tissues (0.872, 95%CI: 0.767-0.977 vs. 0.792, 95%CI: 0.723-0.861). Combined use of circulating hsa_circ_0001821 with the existing tumor markers yielded the largest AUC of 0.933. Finally, hsa_circ_0001821 was demonstrated to mainly locate in the cytoplasm, implying that it played a potential regulatory role in GC at the posttranscriptional level. Conclusion: Hsa_circ_0001821 may prove to be a new and promising potential biomarker for GC diagnosis.

Project description:Gastric cancer is a common malignant tumor with high occurrence and recurrence and is the leading cause of death worldwide. However, the prognostic value of protein-coding and non-coding RNAs in stage III gastric cancer has not been systematically analyzed. In this study, using TCGA data, we identified 585 long noncoding RNAs (lncRNAs) and 927 protein-coding genes (PCGs) correlated with the overall survival rate of gastric cancer. Functional enrichment analysis revealed that the prognostic genes positively correlated with death rates were enriched in pathways, including gap junction, focal adhesion, cell adhesion molecules (CAMs), and neuroactive ligand-receptor interaction, that are involved in the tumor microenvironment and cell-cell communications, suggesting that their dysregulation may promote the tumor progression. To evaluate the performance of the prognostic genes in risk prediction, we built three multivariable Cox models based on prognostic genes selected from the prognostic PCGs and lncRNAs. The performance of the three models based on features from only PCGs or lncRNAs or from all prognostic genes were systematically compared, which revealed that the features selected from all the prognostic genes showed higher performance than the features selected only from lncRNAs or PCGs. Furthermore, the multivariable Cox regression analysis revealed that the stratification with the highest performance was an independent prognostic factor in stage III gastric cancer. In addition, we explored the underlying mechanism of the prognostic lncRNAs in the Cox model by predicting the lncRNA and protein interaction. Specifically, CTD-2218G20.2 was predicted to interact with PSG4, PSG5, and PSG7, which could also interact with cancer-related proteins, including KISS1, TIMP2, MMP11, IGFBP1, EGFR, and CDKN1C, suggesting that CTD-2218G20.2 might participate in the cancer progression via these cancer-related proteins. In summary, the systematic analysis of the prognostic lncRNAs and PCGs was of great importance to the understanding of the progression of stage III gastric cancer.

Project description:Background: Accumulating literature demonstrates that long noncoding RNAs (lncRNAs) are involved in ferroptosis and gastric cancer progression. However, the predictive value of ferroptosis-related lncRNAs for prognosis and therapeutic response is yet to be elucidated in gastric cancer (GC). Method: The transcriptomic data and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The association between ferroptosis-related lncRNAs and ferroptosis regulators was analyzed by Spearman correlation analysis. Then, we established a risk predictive model based on the ferroptosis-related lncRNAs using multivariate Cox regression analysis. Furthermore, we performed correlation analysis for the risk score and characteristics of biological processes, immune landscape, stromal activity, genomic integrity, drug response, and immunotherapy efficacy. Results: We constructed a 17-ferroptosis-related-lncRNA signature via multivariate Cox analysis to divide patients into two groups: low- and high-risk groups. The low-risk group was linked to prolonged overall survival and relapse-free survival. The risk score had good predictive ability to predict the prognosis of GC patients compared with other clinical biomarkers. We found that the high-risk group was associated with activation of carcinogenetic signaling pathways, including stromal activation, epithelial-mesenchymal-transition (EMT) activation, and immune escape through integrated bioinformatics analysis. In contrast, the low-risk group was associated with DNA replication, immune-flamed state, and genomic instability. Additionally, through Spearman correlation analysis, we found that patients in the high-risk group may respond well to drugs targeting cytoskeleton, WNT signaling, and PI3K/mTOR signaling, and drugs targeting chromatin histone acetylation, cell cycle, and apoptosis regulation could bring more benefits for the low-risk group. The high-risk group was associated with poor immunotherapy efficacy. Conclusion: Our study systematically evaluated the role of ferroptosis-related lncRNAs in t tumor microenvironment, therapeutic response, and prognosis of GC. Risk score-based stratification could reflect the characteristic of biological processes, immune landscape, stromal activity, genomic stability, and pharmaceutical profile in GC patients. The ferroptosis-related lncRNA signature could serve as a reliable biomarker to predict prognosis and therapeutic response of patients with GC.

Project description:Melanoma skin cancer remains the leading cause of skin cancer-related deaths. Spitz lesions represent a subset of melanocytic skin lesions characterized by epithelioid or spindled melanocytes organized in nests. These lesions occupy a spectrum ranging from benign Spitz and atypical Spitz lesions all the way to malignant Spitz tumors. Appropriate management is reliant on accurate diagnostic classification, yet this effort remains challenging using current light microscopic techniques. The discovery of novel biomarkers such as microRNAs (miR) may ultimately be a useful diagnostic adjunct for the evaluation of Spitz lesions. miR expression profiles have been suggested for non-Spitz melanomas but have yet to be ascribed to Spitz lesions. We hypothesized that distinct miR expression profiles would be associated with different lesions along the Spitz spectrum.RNAs extracted from paraffin-embedded, formalin-fixed tissues of 11 resected skin lesions including benign nevi (n = 2), benign Spitz lesions (n = 3), atypical Spitz lesions (n = 3), and malignant Spitz tumors (n = 3) were analyzed by the NanoString platform for simultaneous evaluation of over 800 miRs in each patient sample.Benign Spitz lesions had increased expression of miR-21-5p and miR-363-3p compared with those of benign nevi. Malignant Spitz lesions exhibited overexpression of miR-21-5p, miR-155-5p, and miR-1283 relative to both benign nevi and benign Spitz tumors. Notably, atypical Spitz tumors had increased expression of miR-451a and decreased expression of miR-155-5p expression relative to malignant Spitz lesions. Conversely, atypical Spitz lesions had increased expression of miR-21-5p, miR-34a-5p, miR-451a, miR-1283, and miR-1260a relative to benign Spitz tumors.Overall, distinct miR profiles are suggested among Spitz lesions of varying malignant potential with some similarities to non-Spitz melanoma tumors. This work demonstrates the feasibility of this analytic method and forms the basis for further validation studies.

Dataset Information

Global expression profiling of metabolic pathway-related lncRNAs in human gastric cancer and the identification of RP11-555H23.1 as a new diagnostic biomarker.

Publications

Global expression profiling of metabolic pathway-related lncRNAs in human gastric cancer and the identification of RP11-555H23.1 as a new diagnostic biomarker.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets