LncRNA LINC01133 Targeting miR-141-5p to Mediate the Progression and Ameliorate Poor Prognosis of Prostate Cancer DOI
Yang Yang Li,

Y. N. Zhang

Russian Journal of Genetics, Journal Year: 2023, Volume and Issue: 59(S2), P. S191 - S198

Published: Dec. 1, 2023

Language: Английский

LIMD1-AS1 promotes the progression of prostate cancer and affects the function of prostate cancer cells by down-regulating miR-29c-3p DOI Creative Commons

Yongsheng Yu,

Nan He,

Zhaolu Song

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 151(1)

Published: Dec. 5, 2024

Prostate cancer (PCa) is a prevalent and lethal malignancy affecting males, with considerable proportion of patients experiencing poor survival outcomes. The regulatory role LIMD1-AS1 in the initiation progression PCa emerging as significant factor, however, precise mechanisms governing its influence are yet to be fully elucidated. qRT-PCR was employed assess expression miR-29c-3p. Cell Counting Kit-8 (CCK-8) used cell proliferation cells. Apoptosis rates were determined using flow cytometry. migration invasion evaluated transwell assay. targeted relationship miR-29c-3p confirmed through dual-luciferase reporter gene analysis. Increased decreased observed both tumor tissues serum from patients. exhibited diagnostic prognostic significance Functionally, modulated potentiate proliferative, migratory, invasive capabilities cells while concurrently inhibiting apoptosis. LncRNA promotes advancement by regulating miR-29c-3p, indicating that LIMD1-AS1/miR-29c-3p axis could serve potential therapeutic targets for intervention PCa.

Language: Английский

Citations

1

A Group of New Hypermethylated Long Non-Coding RNA Genes Associated with the Development and Progression of Breast Cancer DOI
Е. А. Филиппова, В. И. Логинов, С. С. Лукина

et al.

Molecular Biology, Journal Year: 2024, Volume and Issue: 58(1), P. 71 - 80

Published: Feb. 1, 2024

Language: Английский

Citations

0

A group of new hypermethylated long non-coding RNA genes associated with the development and progression of breast cancer DOI
Е. А. Филиппова, В. И. Логинов, С. С. Лукина

et al.

Молекулярная биология, Journal Year: 2024, Volume and Issue: 58(1), P. 88 - 98

Published: Feb. 15, 2024

Breast cancer is the most common type of among women. The study mechanisms metastasis, main cause death from breast cancer, as well search for new markers early diagnosis and prognosis an extremely topical issue. New perspectives in treatment are opened by gene regulation involving non-coding RNAs, particular, long RNAs (lncRNAs). In this work, we analyzed methylation level seven lncRNA genes (MEG3, SEMA3B-AS1, HAND2-AS1, KCNK15-AS1, ZNF667-AS1, MAGI2-AS3, PLUT) quantitative methyl-specific PCR on a set 79 paired (tumor/normal) samples cancer. Hypermethylation all was revealed, hypermethylation MAGI2-AS3 PLUT detected us first time. It found that studied correlated statistically significantly with stage tumor process, size tumor, presence metastases lymph nodes. Thus, associated development progression these can be useful potential

Language: Английский

Citations

0

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction DOI Creative Commons
Guanghui Li,

Peihao Bai,

Liang Cheng

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 28, 2023

Abstract Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation gene expression, cell differentiation, and development tumors cancers. Predicting relationships between lncRNAs diseases can contribute better understanding pathogenic mechanisms disease provide strong support for advanced treatment methods. Results Therefore, we present an innovative node-adaptive Transformer model predicting unknown associations (GNATLDA). First, utilize feature smoothing (NAFS) method learn local information nodes encode structural fusion similarity network using Structural Deep Network Embedding (SDNE). Next, module, which contains multi-headed attention layer, is used global about heterogeneous network, capture potential association nodes. Finally, employ module with two layers learning global-level embedding fusion. structure coding added as inductive bias compensate missing message-passing mechanism in Transformer. Our accounts both local-level node exploits horizon model, fuses comprehensively investigate unidentified nodes, significantly increasing predictive effectiveness interactions lncRNAs. We conducted case studies on four diseases; 55 out 60 were confirmed by literature. Conclusions proposed GNATLDA serve highly efficient computational associations.

Language: Английский

Citations

0

LncRNA LINC01133 Targeting miR-141-5p to Mediate the Progression and Ameliorate Poor Prognosis of Prostate Cancer DOI
Yang Yang Li,

Y. N. Zhang

Russian Journal of Genetics, Journal Year: 2023, Volume and Issue: 59(S2), P. S191 - S198

Published: Dec. 1, 2023

Language: Английский

Citations

0