NDLI: Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets

Content Provider	Springer Nature : BioMed Central
Author	Asada, Ken Kaneko, Syuzo Takasawa, Ken Shiraishi, Kouya Shinkai, Norio Shimada, Yoko Takahashi, Satoshi Machino, Hidenori Kobayashi, Kazuma Bolatkan, Amina Komatsu, Masaaki Yamada, Masayoshi Miyake, Mototaka Watanabe, Hirokazu Tateishi, Akiko Mizuno, Takaaki Okubo, Yu Mukai, Masami Yoshida, Tatsuya Yoshida, Yukihiro Horinouchi, Hidehito Watanabe, Shun-Ichi Ohe, Yuichiro Yatabe, Yasushi Kohno, Takashi Hamamoto, Ryuji
Abstract	Background The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. Methods Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. Results Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. Conclusions We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.
Related Links	https://molecular-cancer.biomedcentral.com/counter/pdf/10.1186/s12943-024-02093-w.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	14764598
DOI	10.1186/s12943-024-02093-w
Journal	Molecular Cancer
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2024-09-02
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Genomics Epigenomics Lung adenocarcinoma Multi-omics analysis Biomarkers Therapeutic target
Content Type	Text
Resource Type	Correspondence
Subject	Oncology Molecular Medicine Cancer Research
Journal Impact Factor	27.7/2023
5-Year Journal Impact Factor	31.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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