Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

Yang Zhou; Xuezhong Dai; Jianhong Lyu; Yingyue Li; Xueyu Bao; Fang Deng; Kun Liu; Liming Cui; Li Cheng

doi:10.1097/MD.0000000000031075

Construction and validation of a novel prognostic model for thyroid cancer based on N7-methylguanosine modification-related lncRNAs

Medicine (Baltimore). 2022 Oct 21;101(42):e31075. doi: 10.1097/MD.0000000000031075.

Authors

Yang Zhou¹, Xuezhong Dai¹, Jianhong Lyu², Yingyue Li¹, Xueyu Bao¹, Fang Deng¹, Kun Liu³, Liming Cui¹, Li Cheng⁴

Affiliations

¹ Department of Otolaryngology Head and Neck Surgery, The Third People's Hospital of Yunnan Province, Kunming, Yunnan, China.
² Department of Anesthesiology, The Third People's Hospital of Yunnan Province, Kunming, Yunnan, China.
³ Department of Otorhinolaryngology, Puren Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, Hubei, China.
⁴ Department of Endocrinology, The Third People's Hospital of Yunnan Province, Kunming, Yunnan, China.

Abstract

Background: To construct and verify a novel prognostic model for thyroid cancer (THCA) based on N7-methylguanosine modification-related lncRNAs (m7G-lncRNAs) and their association with immune cell infiltration.

Methods: In this study, we identified m7G-lncRNAs using co-expression analysis and performed differential expression analysis of m7G-lncRNAs between groups. We then constructed a THCA prognostic model, performed survival analysis and risk assessment for the THCA prognostic model, and performed independent prognostic analysis and receiver operating characteristic curve analyses to evaluate and validate the prognostic value of the model. Furthermore, analysis of the regulatory relationship between prognostic differentially expressed m7G-related lncRNAs (PDEm7G-lncRNAs) and mRNAs and correlation analysis of immune cells and risk scores in THCA patients were carried out.

Results: We identified 29 N7-methylguanosine modification-related mRNAs and 116 differentially expressed m7G-related lncRNAs, including 87 downregulated and 29 upregulated lncRNAs. Next, we obtained 8 PDEm7G-lncRNAs. A final optimized model was constructed consisting of 5 PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, TMEM105, SMG7-AS1 and HMGA2-AS1). Six PDEm7G-lncRNAs (DOCK9-DT, DPP4-DT, HMGA2-AS1, LINC01976, MID1IP1-AS1, and SMG7-AS1) had positive regulatory relationships with 10 PDEm7G-mRNAs, while 2 PDEm7G-lncRNAs (LINC02026 and TMEM105) had negative regulatory relationships with 2 PDEm7G-mRNAs. Survival curves and risk assessment predicted the prognostic risk in both groups of patients with THCA. Forest maps and receiver operating characteristic curves were used to evaluate and validate the prognostic value of the model. Finally, we demonstrated a correlation between different immune cells and risk scores.

Conclusion: Our results will help identify high-risk or low-risk patients with THCA and facilitate early prediction and clinical intervention in patients with high risk and poor prognosis.

MeSH terms

Carrier Proteins / metabolism
Dipeptidyl Peptidase 4
Gene Expression Profiling / methods
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Prognosis
RNA, Long Noncoding* / genetics
RNA, Long Noncoding* / metabolism
RNA, Messenger / genetics
Thyroid Neoplasms* / genetics

Substances

RNA, Long Noncoding
8-methylguanosine
Dipeptidyl Peptidase 4
RNA, Messenger
SMG7 protein, human
Carrier Proteins