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Risk Stratification and Validation of Eleven Autophagy-Related lncRNAs for Esophageal Squamous Cell Carcinoma

ABSTRACT: Esophageal squamous cell carcinoma (ESCC), the most prevalent subtype of esophageal cancer, ranks sixth in cancer-related mortality, making it one of the deadliest cancers worldwide. The identification of potential risk factors for ESCC might help in implementing precision therapies. Autophagy-related lncRNAs are a group of non-coding RNAs that perform critical functions in the tumor immune microenvironment and therapeutic response. Therefore, we aimed to establish a risk model composed of autophagy-related lncRNAs that can serve as a potential biomarker for ESCC risk stratification. Using the RNA expression profile from 179 patients in the GSE53622 and GSE53624 datasets, we found 11 lncRNAs (AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586) that were co-expressed with autophagy genes and were independent prognostic factors in multivariate Cox regression analysis. The risk model was constructed using these autophagy-related lncRNAs, and the area under the receiver operating characteristic curve (AUC) of the risk model was 0.728. To confirm that the model is reliable, the data of 174 patients from The Cancer Genome Atlas (TCGA) esophageal cancer dataset were analyzed as the testing set. A nomogram for ESCC prognosis was developed using the risk model and clinic-pathological characteristics. Immune function annotation and tumor mutational burden of the two risk groups were analyzed and the high-risk group displayed higher sensitivity in chemotherapy and immunotherapy. Expression of differentially expressed lncRNAs were further validated in human normal esophageal cells and esophageal cancer cells. The constructed lncRNA risk model provides a useful tool for stratifying risk and predicting the prognosis of patients with ESCC, and might provide novel targets for ESCC treatment.

SUBMITTER: Zhao X

PROVIDER: S-EPMC9271611 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and purpose: Radioresistance remains a major reason of radiotherapeutic failure in esophageal squamous cell carcinoma (ESCC). Our study is to screen the immune-related long non-coding RNA (ir-lncRNAs) of radiation-resistant ESCC (rr-ESCC) via Gene Expression Omnibus (GEO) database and to construct a prognostic risk model. Methods: Microarray data (GSE45670) related to radioresistance of ESCC was downloaded from GEO. Based on pathologic responses after chemoradiotherapy, patients were divided into a non-responder (17 samples) and responder group (11 samples), and the difference in expression profiles of ir-lncRNAs were compared therein. Ir-lncRNA pairs were constructed for the differentially expressed lncRNAs as prognostic variables, and the microarray dataset (GSE53625) was downloaded from GEO to verify the effect of ir-lncRNA pairs on the long-term survival of ESCC. After modelling, patients are divided into high- and low-risk groups according to prognostic risk scores, and the outcomes were compared within groups based on the COX proportional hazards model. The different expression of ir-lncRNAs were validated using ECA 109 and ECA 109R cell lines via RT-qPCR. Results: 26 ir-lncRNA genes were screened in the GSE45670 dataset with differential expression, and 180 ir-lncRNA pairs were constructed. After matching with ir-lncRNA pairs constructed by GSE53625, six ir-lncRNA pairs had a significant impact on the prognosis of ESCC from univariate analysis model, of which three ir-lncRNA pairs were significantly associated with prognosis in multivariate COX analysis. These three lncRNA pairs were used as prognostic indicators to construct a prognostic risk model, and the predicted risk scores were calculated. With a median value of 2.371, the patients were divided into two groups. The overall survival (OS) in the high-risk group was significantly worse than that in the low-risk group (p < 0.001). The 1-, 2-, and 3-year prediction performance of this risk-model was 0.666, 0.702, and 0.686, respectively. In the validation setting, three ir-lncRNAs were significantly up-regulated, while two ir-lncRNAs were obviouly down-regulated in the responder group. Conclusion: Ir-lncRNAs may be involved in the biological regulation of radioresistance in patients with ESCC; and the prognostic risk-model, established by three ir-lncRNAs pairs has important clinical value in predicting the prognosis of patients with rr-ESCC.

Project description:Background The study aimed to develop a nomogram model to predict overall survival (OS) and construct a risk stratification system of upper thoracic esophageal squamous cell carcinoma (ESCC). Methods Newly diagnosed 568 patients with upper ESCC at Fujian Medical University Cancer Hospital were taken as a training cohort, and additional 155 patients with upper ESCC from Sichuan Cancer Hospital Institute were used as a validation cohort. A nomogram was established using Cox proportional hazard regression to identify prognostic factors for OS. The predictive power of nomogram model was evaluated by using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI). Results In this study, multivariate analysis revealed that gender, clinical T stage, clinical N stage and primary gross tumor volume were independent prognostic factors for OS in the training cohort. The nomogram based on these factors presented favorable prognostic efficacy in the both training and validation cohorts, with concordance statistics (C-index) of 0.622, 0.713, and area under the curve (AUC) value of 0.709, 0.739, respectively, which appeared superior to those of the American Joint Committee on Cancer (AJCC) staging system. Additionally, net reclassification index (NRI) and integrated discrimination improvement (IDI) of the nomogram presented better discrimination ability to predict survival than those of AJCC staging. Furthermore, decision curve analysis (DCA) of the nomogram exhibited greater clinical performance than that of AJCC staging. Finally, the nomogram fairly distinguished the OS rates among low, moderate, and high risk groups, whereas the OS curves of clinical stage could not be well separated among clinical AJCC stage. Conclusion We built an effective nomogram model for predicting OS of upper ESCC, which may improve clinicians’ abilities to predict individualized survival and facilitate to further stratify the management of patients at risk.

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Risk Stratification and Validation of Eleven Autophagy-Related lncRNAs for Esophageal Squamous Cell Carcinoma

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