İçeriğe geç
akaturk Akademik ölçüm

Makale detayı · 2025 · article

In Silico Identification of LSD1 Inhibition-Responsive Targets in Small Cell Lung Cancer

ISSN2306-5354
YÖKSİS OpenAlex Açık erişim · gold
Yıl2025
Atıf2OpenAlex
Yüzdelik%72,6
FWCI0,841,00 = dünya ortalaması
Scopus (SJR)Q2
WoS (JCR)Q2

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıBioengineering
  • Katalog eşleşmesi (ISSN)Bioengineering
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)
  • Semantic Scholaratıf sayısı (OpenAlex ile birleştirilmez)

Özet

OpenAlex İngilizce

Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid progression, high metastatic potential, and limited therapeutic options. Lysine-specific demethylase 1 (LSD1) has been identified as a promising epigenetic target in SCLC. RG6016 (ORY-1001) is a selective LSD1 inhibitor currently under clinical investigation for its antitumor activity. In this study, publicly available RNA-Seq datasets from SCLC patient-derived xenograft (PDX) models treated with RG6016 were reanalyzed using bioinformatic approaches. Differential gene expression analysis was conducted to identify genes responsive to LSD1 inhibition. Candidate genes showing significant downregulation were further evaluated by molecular docking to assess their potential interaction with RG6016. The analysis identified a set of differentially expressed genes following RG6016 treatment, including notable downregulation of MYC, UCHL1, and TSPAN8. In silico molecular docking revealed favorable docking poses between RG6016 and the proteins encoded by these genes, suggesting potential direct or indirect targeting. These findings support a broader mechanism of action for RG6016 beyond its known interaction with LSD1. This study demonstrates that RG6016 may exert its antitumor effects through the modulation of additional molecular targets such as MYC, UCHL1, and TSPAN8 in SCLC. The combined bioinformatic and molecular docking analyses provide new insights into the potential multi-target profile of RG6016 and indicate the need for further experimental validation.

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

2atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yerel katalogda bu makaleye atıf yapan 3 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. 2025 Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic DataAtıf 1 · OpenAlex
  2. 2025 Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic DataAtıf 1 · OpenAlex
  3. 2025 Integrative Machine Learning Model for Overall Survival Prediction in Breast Cancer Using Clinical and Transcriptomic DataAtıf 1 · OpenAlex

Yazarlar

4
  1. İHSAN NALKIRAN RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ 1
  2. HATİCE SEVİM NALKIRAN RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ 2
  3. NESLİHAN ÖZÇELİK 3
  4. MEHMET KIVRAK 4