Development and Application of Accurate Detection Technology Based on Multimodal Data of Breast Cancer Comobid Depression
This study is a prospective, observational clinical research aimed at establishing a multimodal database encompassing clinical information and gut microbiome data from a sample of over 1,000 breast cancer patients comorbid with depression. The research involves collecting cohort sample information from breast cancer patients with comorbid depression, as well as fecal, blood, and saliva specimens for metagenomic sequencing, untargeted metabolite detection, and cortisol level analysis, respectively. Based on the collected multimodal data, diagnostic, efficacy prediction, and prognostic survival prediction models for breast cancer with comorbid depression will be developed. Additionally, a precision prediction cloud platform will be designed and deployed to support data upload, model prediction, and result visualization.
• Pathologically confirmed primary breast cancer; Aged 18-80 years; No prior history of malignancy other than breast cancer; Awareness of their breast cancer diagnosis; Karnofsky Performance Status (KPS) score \>70; Willing to provide blood, fecal, and saliva samples with signed informed consent.