An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients
Purpose. Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Genetics Research |
| Online Access: | http://dx.doi.org/10.1155/2022/3895396 |
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| _version_ | 1832565319771619328 |
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| author | Jiahui Tian Chunyan Fu Xuan Zeng Xiaoxiao Fan Yi Wu |
| author_facet | Jiahui Tian Chunyan Fu Xuan Zeng Xiaoxiao Fan Yi Wu |
| author_sort | Jiahui Tian |
| collection | DOAJ |
| description | Purpose. Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival. Methods. We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets. Results. Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (P<0.001). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched. Conclusion. A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC. |
| format | Article |
| id | doaj-art-9b2e2e94a0084b3cbc3b14adea610577 |
| institution | Kabale University |
| issn | 1469-5073 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Genetics Research |
| spelling | doaj-art-9b2e2e94a0084b3cbc3b14adea6105772025-02-03T01:08:45ZengWileyGenetics Research1469-50732022-01-01202210.1155/2022/3895396An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer PatientsJiahui Tian0Chunyan Fu1Xuan Zeng2Xiaoxiao Fan3Yi Wu4Department of LaboratoryDepartment of MedicineDepartment of MedicineDepartment of LaboratoryDepartment of LaboratoryPurpose. Pancreatic cancer (PC) is a common, highly lethal cancer with a low survival rate. Autophagy is involved in the occurrence and progression of PC. This study aims to explore the feasibility of using an autophagy-related long noncoding RNA (lncRNA) signature for assessing PC patient survival. Methods. We obtained RNA sequencing and clinical data of patients from the TCGA website. Autophagy genes were obtained from the Human Autophagy Database. The prognostic model, generated through univariate and multivariate Cox regression analyses, included 10 autophagy-related lncRNAs. Receiver operating characteristic (ROC) curves and forest plots were generated for univariate and multivariate Cox regression analyses, to examine the predictive feasibility of the risk model. Gene set enrichment analysis (GSEA) was used to screen enriched gene sets. Results. Twenty-eight autophagy-related lncRNAs were filtered out through univariate Cox regression analysis (P<0.001). Ten autophagy-related lncRNAs, including 4 poor prognosis factors and 6 beneficial prognosis factors, were further screened via multivariate Cox regression analysis. The AUC value of the ROC curve was 0.815. GSEA results demonstrated that cancer-related gene sets were significantly enriched. Conclusion. A signature based on ten autophagy-related lncRNAs was identified. This signature could be potentially used for evaluating clinical prognosis and might be used for targeted therapy against PC.http://dx.doi.org/10.1155/2022/3895396 |
| spellingShingle | Jiahui Tian Chunyan Fu Xuan Zeng Xiaoxiao Fan Yi Wu An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients Genetics Research |
| title | An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients |
| title_full | An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients |
| title_fullStr | An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients |
| title_full_unstemmed | An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients |
| title_short | An Independent Prognostic Model Based on Ten Autophagy-Related Long Noncoding RNAs in Pancreatic Cancer Patients |
| title_sort | independent prognostic model based on ten autophagy related long noncoding rnas in pancreatic cancer patients |
| url | http://dx.doi.org/10.1155/2022/3895396 |
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