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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35832188", "kind": "science-record", "revision": 3, "content_hash": "sha256:d952ea8249cd642369012324edf788858217ca8a7f212e0b7d5da3fc8e4423ab", "created_at": "2026-11-01T18:14:53Z", "updated_at": "2026-11-01T21:28:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Risk Stratification and Validation of Eleven Autophagy-Related lncRNAs for Esophageal Squamous Cell Carcinoma.", "doi": "10.3389/fgene.2022.894990", "pmid": "35832188", "authors": ["Xu Zhao", "Yulun Wang", "Fanbiao Meng", "Zhuang Liu", "Bo Xu"], "journal": "Frontiers in genetics", "year": 2022, "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.", "claims": [{"id": "claim-method", "statement": "Of these, 11 lncRNAs were identified as independent prognostic lncRNAs using multivariate Cox regression, including AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586 (Table 2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35832188", "locator": "sections[14] \"Screening of Prognostic Long Non-Coding RNAs\" paragraph 2", "quote": "Of these, 11 lncRNAs were identified as independent prognostic lncRNAs using multivariate Cox regression, including AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586 (Table 2)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35832188", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "To evaluate the performance of the risk model, we also drew a multi-index ROC curve (Figure 3B), with an AUC value of 0.728, which was higher than that of other clinical characteristics, indicating that the constructed risk model has the best value for predicting the outcome of patients with ESCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35832188", "locator": "sections[16] \"Development of a Prognostic Autophagy Long Non-Coding RNAs Signature in Esophageal Squamous Cell Carcinoma\" paragraph 4", "quote": "To evaluate the performance of the risk model, we also drew a multi-index ROC curve (Figure 3B), with an AUC value of 0.728, which was higher than that of other clinical characteristics, indicating that the constructed risk model has the best value for predicting the outcome of patients with ESCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "To evaluate the performance of the risk model, we also drew a multi-index ROC curve (Figure 3B), with an AUC value of 0.728, which was higher than that of other clinical characteristics, indicating that the constructed risk model has the best value for predicting the outcome of patients with ESCC.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35832188"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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