Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine

Ye, Lele1,2; Tong, Xinya1; Pan, Kan3; Shi, Xinyu4; Xu, Binbing3; Yao, Xuyang3; Zhuo, Linpei5; Fang, Su1; Tang, Sangsang2; Jiang, Zhuofeng6; Xue, Xiangyang1; Lu, Weiguo2,7,8; Guo, Gangqiang1

2024-06-04

10.3389/fphar.2024.1351929

FRONTIERS IN PHARMACOLOGY   影响因子和分区

1663-9812

Background: Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m(6)A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m(6)A modification-associated lncRNAs. Methods: Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m(6)A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Results: Six m(6)A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m(6)A effector-related lncRNA risk model (m(6)A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m(6)A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m(6)A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m(6)A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. Conclusion: In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m(6)A-related lncRNAs in SOC. Incorporating the m(6)A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

m(6)A modification immunotherapy biomarker predictive preventive personalized medicine (PPPM/3PM)

SCI

英语

其他

National Natural Science Foundation of China[32200476] ; Zhejiang Provincial Natural Science Foundation of China[LQ23C060002] ; Wenzhou Municipal Science and Technology Bureau[Y20220044]

Pharmacology & Pharmacy

Pharmacology & Pharmacy

WOS:001249973000001

FRONTIERS MEDIA SA

Web of Science

1.Wenzhou Med Univ, Wenzhou Collaborat Innovat Ctr Gastrointestinal Ca, Inst Mol Virol & Immunol,Dept Microbiol & Immunol,, Inst Trop Med,Wenzhou Key Lab Canc Related Pathoge, Wenzhou, Zhejiang, Peoples R China
2.Zhejiang Univ, Womens Hosp, Womens Reprod Hlth Lab Zhejiang Prov, Sch Med, Hangzhou, Zhejiang, Peoples R China
3.Wenzhou Med Univ, Clin Coll 1, Wenzhou, Zhejiang, Peoples R China
4.Zhejiang Univ, Sch Med, Inst Immunol, Hangzhou, Zhejiang, Peoples R China
5.Kunming Med Univ, Haiyuan Coll, Kunming, Yunnan, Peoples R China
6.Southern Univ Sci & Technol, Sch Med, Dept Biochem, Shenzhen, Guangdong, Peoples R China
7.Zhejiang Univ, Sch Med, Womens Hosp, Dept Gynecol Oncol, Hangzhou, Zhejiang, Peoples R China
8.Ctr Uterine Canc Diag & Therapy Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China

Ye, Lele,Tong, Xinya,Pan, Kan,et al. Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine[J]. FRONTIERS IN PHARMACOLOGY,2024,15.

Ye, Lele.,Tong, Xinya.,Pan, Kan.,Shi, Xinyu.,Xu, Binbing.,...&Guo, Gangqiang.(2024).Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.FRONTIERS IN PHARMACOLOGY,15.

Ye, Lele,et al."Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine".FRONTIERS IN PHARMACOLOGY 15(2024).