A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis DOI
Hossain Shadman,

Saghar Gomrok,

Qianyi Cheng

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 24, 2024

Integrins, a family of transmembrane receptor proteins, play complex roles in cancer development and metastasis. These could be better delineated through machine learning transcriptomic data to reveal relationships between integrin expression patterns cancer.

Язык: Английский

A machine learning-based investigation of integrin expression patterns in cancer and metastasis DOI Creative Commons
Hossain Shadman,

Saghar Gomrok,

Christopher Litle

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 12, 2025

Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, comprehensive understanding these has not been achieved due the complex relationships between specific integrins, types, stages progression. Publicly accessible repositories from Genotype-Tissue Expression (GTEx) The Cancer Genome Atlas (TCGA) projects provide rich datasets for exploring using machine learning (ML). In this study, integrin RNA-Seq expression data ~ 8 healthy tissues GTEx corresponding tumors TCGA were selected. Integrin was used train ML models distinguish different tissues, solid tumors, as normal tumor samples same tissue type. These can classify by origin or disease status with high accuracy, integrins essential classifiers identified. some cases, only one two needed type, type accuracy > 0.9. For example, ITGA7 alone cancerous breast tissue. Additionally, co-expression networks compared found change significantly cancer, indicating changes functional involvement cancer. metastatic further examined AURORA project Metastatic Breast (MBC), several such ITGAD, ITGA4, ITGAL, ITGA11 have lower metastases than primary tumors.

Язык: Английский

Процитировано

0

Exosomal Integrin Alpha 3 Promotes Epithelial Ovarian Cancer Cell Migration via the S100A7/p-ERK Signaling Pathway DOI Creative Commons

Zeyuan Yin,

Jiachen Ma, Joseph Adu‐Amankwaah

и другие.

Acta Biochimica et Biophysica Sinica, Год журнала: 2025, Номер unknown

Опубликована: Фев. 19, 2025

Epithelial ovarian cancer (EOC) is a highly aggressive malignancy with poor prognosis due to late-stage diagnosis and the lack of reliable biomarkers for early detection. Exosomes, small vesicles involved in intercellular communication, play critical role progression by promoting migration, proliferation, metastasis. This study investigates exosomal proteins EOC cell migration identifies potential biomarkers. Exosomes are isolated from ascites fluid patients (C-Exos) benign disease (B-Exos), mass spectrometry analysis clinical samples reveals 185 differentially expressed proteins, integrin alpha 3 (ITGA3) being strongly associated prognosis. ITGA3 transported via exosomes recipient cells, where it released into cytoplasm translocated membrane. localization enables activate intracellular signaling pathways that drive migration. Immunoprecipitation may influence through S100A7/p-ERK pathway. Mechanistically, activates ERK S100A7, In vivo, enrich facilitates tumor growth whereas knockdown reduces these effects. These findings suggest ITGA3, pathway, promotes could serve as prognostic biomarker therapeutic target EOC. Targeting ITGA3/S100A7 axis help suppress suggesting promising strategy improve patient outcomes.

Язык: Английский

Процитировано

0

A Machine Learning-Based Investigation of Integrin Expression Patterns in Cancer and Metastasis DOI
Hossain Shadman,

Saghar Gomrok,

Qianyi Cheng

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Сен. 24, 2024

Integrins, a family of transmembrane receptor proteins, play complex roles in cancer development and metastasis. These could be better delineated through machine learning transcriptomic data to reveal relationships between integrin expression patterns cancer.

Язык: Английский

Процитировано

1