Dataset Information

A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer.

ABSTRACT: Autophagy is an important bioprocess throughout the occurrence and development of cancer. However, the role of autophagy-related lncRNAs in pancreatic cancer (PC) remains obscure. In the study, we identified the autophagy-related lncRNAs (ARlncRNAs) and divided the PC patients from The Cancer Genome Atlas into training and validation set. Firstly, we constructed a signature in the training set by the least absolute shrinkage and selection operator penalized cox regression analysis and the multivariate cox regression analysis. Then, we validated the independent prognostic role of the risk signature in both training and validation set with survival analysis, receiver operating characteristic analysis, and Cox regression. The nomogram was established to demonstrate the predictive power of the signature. Moreover, high risk scores were significantly correlated to worse outcomes and severe clinical characteristics. The Pearson's analysis between risk scores with immune cells infiltration, tumor mutation burden, and the expression level of chemotherapy target molecules indicated that the signature could predict efficacy of immunotherapy and targeted therapy. Next, we constructed an lncRNA-miRNA-mRNA regulatory network and identified several potential small molecule drugs in the Connectivity Map (CMap). What's more, quantitative real-time PCR (qRT-PCR) analysis showed that serum LINC01559 could serve as a diagnostic biomarker. In vitro analysis showed inhibition of LINC01559 suppressed PC cell proliferation, migration, and invasion. Additionally, silencing LINC01559 suppressed gemcitabine-induced autophagy and promoted the sensitivity of PC cells to gemcitabine. In conclusion, we identified a novel ARlncRNAs signature with valuable clinical utility for reliable prognostic prediction and personalized treatment of PC patients. And inhibition of LINC01559 might be a novel strategy to overcome chemoresistance.

SUBMITTER: Deng Z

PROVIDER: S-EPMC7769875 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer.

Deng Zhengdong Z Li Xiangyu X Shi Yuanxin Y Lu Yun Y Yao Wei W Wang Jianming J

Frontiers in cell and developmental biology 20201215

Autophagy is an important bioprocess throughout the occurrence and development of cancer. However, the role of autophagy-related lncRNAs in pancreatic cancer (PC) remains obscure. In the study, we identified the autophagy-related lncRNAs (ARlncRNAs) and divided the PC patients from The Cancer Genome Atlas into training and validation set. Firstly, we constructed a signature in the training set by the least absolute shrinkage and selection operator penalized cox regression analysis and the multiv ...[more]

PMID: 33384999

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Project description:BackgroundAutophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.MethodsFirst, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.ResultsA total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.ConclusionsBased on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

Project description:Pancreatic cancer (PC) is a malignant tumor of the digestive system with a poor prognosis. PC patients with pancreatitis have a worse prognosis. But nobody reported the relationship between inflammation and prognosis in PC. Based on this, we are going to explore inflammation-related prognostic signature to predict patients' survival and potential therapeutic target. We screened gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to identify differentially expressed genes (DEGs) between tumor and normal tissues with P value < .05. Univariate and multivariate Cox regression analysis was applied to identify possible prognostic inflammation genes and establish an inflammation-related risk score system, which was validated by Kaplan-Meier and Receiver operating characteristic (ROC) curves. Finally, we used the TISIDB database to predict targeted drugs for up-regulated gene hepatocyte growth factor receptor (MET) and used AUTODOCK software for molecular docking. We built a prognostic model consisted of 3 inflammation-related genes (tumor necrosis factor receptor associated factor 1/TFAR1, tyrosine kinase 2/TYK2, MET). According to the median value of those genes' risk score, PC patients were ranked into high- (88) and low-risk (89) groups. Then, the results of the Kaplan-Meier curves and the area under the curve (AUC) of the ROC curves showed this model had a good predictive power (P < .001, AUC = 0.806). The result of human protein atlas (HPA) database showed the expression of TRAF1 and TYK2 were low in pancreatic cancer, the expression of MET was high. TISIDB database founded brigatinib could target to MET. And AUTODOCK showed brigatinib had a nice docking with MET. Taken together, our study suggested that inflammation-associated prognostic signature might be used as novel biomarkers for predicting prognosis in PC patients and potential therapeutic target of the disease.

Project description:Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer. Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes. Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature. Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Dataset Information

A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer.

Publications

A Novel Autophagy-Related IncRNAs Signature for Prognostic Prediction and Clinical Value in Patients With Pancreatic Cancer.

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