Zobrazit v EDS

Integrative multiomics analysis reveals the subtypes and key mechanisms of platinum resistance in gastric cancer: identification of KLF9 as a promising therapeutic target

Uloženo v:
Podrobná bibliografie
Název: Integrative multiomics analysis reveals the subtypes and key mechanisms of platinum resistance in gastric cancer: identification of KLF9 as a promising therapeutic target
Autoři: Pengcheng Zhang, Lexin Wang, Haonan Lin, Yihui Han, Jingfang Zhou, Hang Song, Peng Wang, Huanhuan Tan, Yajuan Fu
Zdroj: Journal of Translational Medicine, Vol 23, Iss 1, Pp 1-21 (2025)
Informace o vydavateli: BMC, 2025.
Rok vydání: 2025
Sbírka: LCC:Medicine
Témata: Gastric cancer, Platinum resistance, Similarity network fusion, Spatial transcriptomics, KLF9, Medicine
Popis: Abstract Background Gastric cancer (GC) is characterized by significant intertumoral heterogeneity, which often leads to the development of resistance to platinum-based chemotherapy. Combining platinum drugs with other therapeutic strategies may improve treatment efficacy; however, the mechanisms underlying platinum resistance in GC remain unclear. Methods Key genes related to platinum resistance in GC were selected from the platinum resistance gene database and GC resistance datasets. The Similarity Network Fusion (SNF) algorithm was employed, along with prognosis-related methylation data and somatic mutation data, to classify the molecular subtypes of GC based on GC platinum resistance genes. Gene expression profiles, prognosis, immune cell infiltration, chemotherapy sensitivity, and immunotherapy responsiveness were comprehensively evaluated for each subtype. Localization and functional evaluation were conducted at the single-cell and spatial transcriptomics levels, and predictive models were developed using machine learning techniques. These functional differences in platinum resistance gene models were further explored in GC. Moreover, experimental validation was conducted to elucidate the mechanisms of key genes involved in platinum resistance in GC. Results Stomach adenocarcinoma (STAD) patients were classified into three subtypes using the SNF algorithm and multiomics data. Patients with subtype CS2 exhibited a significantly poorer prognosis than those with subtypes CS1 and CS3 (p
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 1479-5876
Relation: https://doaj.org/toc/1479-5876
DOI: 10.1186/s12967-025-06725-7
Přístupová URL adresa: https://doaj.org/article/8ef5c1bdf4ae47cfa06b56e8b57f70b9
Přístupové číslo: edsdoj.8ef5c1bdf4ae47cfa06b56e8b57f70b9
Databáze: Directory of Open Access Journals
Popis
Abstrakt:Abstract Background Gastric cancer (GC) is characterized by significant intertumoral heterogeneity, which often leads to the development of resistance to platinum-based chemotherapy. Combining platinum drugs with other therapeutic strategies may improve treatment efficacy; however, the mechanisms underlying platinum resistance in GC remain unclear. Methods Key genes related to platinum resistance in GC were selected from the platinum resistance gene database and GC resistance datasets. The Similarity Network Fusion (SNF) algorithm was employed, along with prognosis-related methylation data and somatic mutation data, to classify the molecular subtypes of GC based on GC platinum resistance genes. Gene expression profiles, prognosis, immune cell infiltration, chemotherapy sensitivity, and immunotherapy responsiveness were comprehensively evaluated for each subtype. Localization and functional evaluation were conducted at the single-cell and spatial transcriptomics levels, and predictive models were developed using machine learning techniques. These functional differences in platinum resistance gene models were further explored in GC. Moreover, experimental validation was conducted to elucidate the mechanisms of key genes involved in platinum resistance in GC. Results Stomach adenocarcinoma (STAD) patients were classified into three subtypes using the SNF algorithm and multiomics data. Patients with subtype CS2 exhibited a significantly poorer prognosis than those with subtypes CS1 and CS3 (p
ISSN:14795876
DOI:10.1186/s12967-025-06725-7