{"doi":"10.18632/aging.205090","title":"A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer","abstract":"BACKGROUND: The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC). METHODS: Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature. RESULTS: Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy. CONCLUSION: The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.","journal":"Aging","year":2023,"id":366207,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":813561,"name":"Pengpeng Zhang","orcid":"0000-0003-1488-265X","position":1,"is_corresponding":false},{"id":1122884,"name":"Shengyi Zhang","orcid":null,"position":2,"is_corresponding":false},{"id":926980,"name":"Wenhui Chen","orcid":"0000-0001-7436-9669","position":3,"is_corresponding":false},{"id":1122441,"name":"Hao Chi","orcid":"0000-0002-5210-0770","position":4,"is_corresponding":false},{"id":864392,"name":"Wei Wang","orcid":"0000-0002-1614-0759","position":5,"is_corresponding":false},{"id":1122442,"name":"Wei Zhang","orcid":"0000-0003-2097-6089","position":6,"is_corresponding":false},{"id":1122443,"name":"Haoran Lin","orcid":"0009-0002-2776-5487","position":7,"is_corresponding":false},{"id":454941,"name":"Yue Yu","orcid":"0000-0001-8266-641X","position":8,"is_corresponding":false},{"id":1122883,"name":"Qianhe Ren","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:14:55.198227Z","pmid":"37812215","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}