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Identification of long non-coding RNA signature for paclitaxel-resistant patients with advanced ovarian cancer.

ABSTRACT: Ovarian cancer is the most lethal gynecologic malignancy, characterized by late diagnosis, frequent relapse, and easy development of chemoresistance. Recent studies suggest that lncRNAs are involved in ovarian cancer onset and progression, as well as the resistance in paclitaxel-containing chemotherapy. However, the genome-wide expression pattern and associated functional implications of lncRNAs in paclitaxel-resistant ovarian cancer cells remain undetermined. In the present study, we identified a panel of lncRNAs aberrantly expressed in both paclitaxel resistant ovarian cancer tissues and cell lines, including XR_948297, XR_947831, XR_938728, XR_938392, NR_103801, NR_073113, and NR_036503. Moreover, the seven-lncRNA signature showed a relatively high predictive accuracy of chemoresistance with an area under the ROC curve (AUC) of 0.93, and was associated with progression-free survival inovarian cancer patients (HR=2.05, p=0.015). Our function prediction demonstrated that the seven-lncRNA signature was positively correlated with a cluster containing 129 genes enriched in insulin secretion-related pathway. Our findings suggest that the seven-lncRNA signature may be utilized as potent biomarkers for predicting chemoresistance for ovarian cancer patients with paclitaxel-containing chemotherapy.

SUBMITTER: Wang L

PROVIDER: S-EPMC5609994 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Identification of long non-coding RNA signature for paclitaxel-resistant patients with advanced ovarian cancer.

Wang Luqing L Hu Yanjun Y Xiang Xiaohong X Qu Kai K Teng Yue Y

Oncotarget 20170802 38

Ovarian cancer is the most lethal gynecologic malignancy, characterized by late diagnosis, frequent relapse, and easy development of chemoresistance. Recent studies suggest that lncRNAs are involved in ovarian cancer onset and progression, as well as the resistance in paclitaxel-containing chemotherapy. However, the genome-wide expression pattern and associated functional implications of lncRNAs in paclitaxel-resistant ovarian cancer cells remain undetermined. In the present study, we identified ...[more]

PMID: 28969062

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Project description:BackgroundOvarian cancer (OC) is the most malignant tumor with the worst prognosis in female reproductive system. Mitophagy and long non-coding RNAs (lncRNAs) play pivotal roles in tumorigenesis, development, and drug resistance. The effects of mitophagy-related lncRNAs on OC prognosis and therapeutic response remain unelucidated.MethodsWe retrieved OC-related RNA sequence, copy number variation, somatic mutation, and clinicopathological information from The Cancer Genome Atlas database and mitophagy-related gene sets from the Reactome database. Pearson's correlation analysis was used to distinguish mitophagy-related lncRNAs. A prognostic lncRNA signature was constructed using UniCox, LASSO, and forward stepwise regression analysis. Individuals with a risk score above or below the median were classified as high- or low-risk groups, respectively. The risk model was analyzed using the Kaplan-Meier estimator, receiver operating characteristic curve, decision curve analysis, and Cox regression analysis and validated using an internal dataset. LINC00174 was validated in clinical samples and OC cell lines. We also reviewed reports on the role of LINC00174 in cancer. Subsequently, a nomogram model was constructed. Furthermore, the Genomics of Drug Sensitivity in Cancer database was used to explore the relationship between the risk model and anti-tumor drug sensitivity. Gene set variation analysis was performed to assess potential differences in biological functions between the two groups. Finally, a lncRNA prognostic signature-related competing endogenous RNA (ceRNA) network was constructed.ResultsThe prognostic signature showed that patients in the high-risk group had a poorer prognosis. The nomogram exhibited satisfactory accuracy and predictive potential. LINC00174 mainly acts as an oncogene in cancer and is upregulated in OC; its knockdown inhibited the proliferation and migration, and promoted apoptosis of OC cells. High-risk patients were more insensitive to cisplatin and olaparib than low-risk patients. The ceRNA network may help explore the potential regulatory mechanisms of lncRNAs.ConclusionThe mitophagy-related lncRNA signature can help estimate the survival and drug sensitivity, the ceRNA network may provide novel therapeutic targets for patients with OC.

Project description:Aim: To identify the pyroptosis-related long non-coding RNAs (lncRNAs) in ovarian cancer and construct a prognostic signature based on them. Methods: Expression data from TCGA was used to explore differentially expressed pyroptosis-related lncRNAs in ovarian cancer. A risk signature was established by LASSO and cox regression analysis and then validated. Databases such as ESTIMATE, CIBERSORT, TIMER, XCELL were used to identify the relation between this signature and the immune microenvironment of ovarian cancer. Gene Set Enrichment Analysis was introduced to identify the pathways and functions that the signature may participate in. Based on miRcode and starBase databases, microRNAs related to the lncRNAs in our signature and the positively co-expressed pyroptosis- related genes were screened and a competing endogenous RNA (ceRNA) network was then constructed. Quantitative reverse transcription PCR was conducted to validate the expression levels of two lncRNAs in this ceRNA network. Results: A 13 pyroptosis-related lncRNA prognostic signature (MYCNOS, AL161772.1, USP30-AS1, ZNF32-AS2, AC068733.3, AC012236.1, AC015802.5, KIAA1671-AS1, AC013403.2, MIR223HG, KRT7-AS, PTPRD-AS1 and LINC01094) was constructed. Patients in high-risk group had a significantly worse prognosis than that of low-risk (P<0.0001). Immune infiltration analysis found that patients identified as high-risk had a higher infiltration of macrophages and tumor-associated fibroblasts. Further pathway analysis revealed that the signature may be involved in epithelial mesenchymal transition, extracellular matrix receptor interaction, and focal adhesion. Finally, a competitive endogenous inhibition relationship was discovered between LINC01094, KRT7-AS, MYCNOS, ZNF32-AS2, AC012236.1 and pyroptosis- related genes such as IRF1, NOD1, GSDMC, NLRP1, PLCG1, GSDME and GZMB, in which LINC01094 and KRT7-AS were found to be overexpressed in three ovarian cancer cell lines. Conclusion: We constructed a pyroptosis-related lncRNA signature and correlate it to the immune microenvironment. A ceRNA regulatory network related to pyroptosis was also constructed, which provides novel insights useful for the study of pyroptosis in ovarian cancer.

Project description:BACKGROUND Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer. MATERIAL AND METHODS The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs. RESULTS A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism. CONCLUSIONS Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.

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Identification of long non-coding RNA signature for paclitaxel-resistant patients with advanced ovarian cancer.

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Identification of long non-coding RNA signature for paclitaxel-resistant patients with advanced ovarian cancer.

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