Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer

Li, Haili1; Zheng, Xubin1,2; Gao, Jing3,4,5; Leung, Kwong-Sak2; Wong, Man-Hon2; Yang, Shu1; Liu, Yakun6; Dong, Ming7; Bai, Huimin8; Ye, Xiufeng1; Cheng, Lixin1

2022-09-01

10.1016/j.compbiomed.2022.105881

COMPUTERS IN BIOLOGY AND MEDICINE   Impact Factor & Quartile

0010-4825

1879-0534

The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.

Serous ovarian cancer Transcriptome circRNA ceRNA Biomarker Gene pair

SCI

English

First ; Corresponding

Guangdong Basic and Applied Basic Research Foundation[2022A1515012368]

Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology

Biology ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology

WOS:000888192600003

PERGAMON-ELSEVIER SCIENCE LTD

COMPUTER SCIENCE

Web of Science

1.Southern Univ Sci & Technol, Jinan Univ, Shenzhen Peoples Hosp, Affiliated Hosp 1,Clin Med Coll 2, Shenzhen, Peoples R China
2.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
3.Univ Helsinki, Dept Pulm Med, Helsinki, Finland
4.Helsinki Univ Hosp, Helsinki, Finland
5.Karolinska Inst, Dept Med, Resp Med Unit, Stockholm, Sweden
6.Hebei Med Univ, Dept Gynecol, Hosp 4, Shijiazhuang, Hebei, Peoples R China
7.Bioland Lab Guangzhou Regenerat Med & Hlth Guangd, Guangzhou, Guangdong, Peoples R China
8.Capital Med Univ, Beijing Chaoyang Hosp, Dept Obstet & Gynecol, Beijing, Peoples R China

Li, Haili,Zheng, Xubin,Gao, Jing,et al. Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2022,148.

Li, Haili.,Zheng, Xubin.,Gao, Jing.,Leung, Kwong-Sak.,Wong, Man-Hon.,...&Cheng, Lixin.(2022).Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer.COMPUTERS IN BIOLOGY AND MEDICINE,148.

Li, Haili,et al."Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer".COMPUTERS IN BIOLOGY AND MEDICINE 148(2022).