Hepatocellular Carcinoma
Conditions
Brief summary
Application of Integrated Proteomic and Serum Metabolomic Analysis in Efficacy and Prognosis Assessment: A multi-omics analysis based on gut microbiota to evaluate the predictive value of microbial-derived proteins and metabolites on treatment efficacy and patient outcomes, developing non-invasive tools for treatment monitoring and prognostic prediction.
Detailed description
This study is a prospective, observational study based on real-world data. It prospectively and continuously collects data from patients with unresectable hepatocellular carcinoma who have received TACE combined with targeted and immunotherapy as part of their routine diagnostic and treatment procedures. Patients are grouped based on treatment efficacy, and integrated proteomic and serum metabolomic analyses are conducted on samples before and during treatment to obtain clinical evidence from the real world.
Interventions
None listed
Sponsors
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years. * Histologically or clinically diagnosed with hepatocellular carcinoma (HCC). * Classified as BCLC stage B/C, not suitable for surgical resection or liver transplantation. * Planned or already receiving TACE combined with tiragolumab and first-line targeted therapy for liver cancer. * Not participating in other clinical studies. * Able to obtain imaging evaluation data and other clinical records during treatment.
Exclusion criteria
* Known fibrolamellar HCC, sarcomatoid HCC, or mixed hepatocellular-cholangiocarcinoma histology. * Tumors involving the main portal vein or inferior vena cava. * Use of antibiotics within one month before treatment. * History of other malignancies.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Differences in Macroproteomics and Serum Metabolomics Expression between Effective and Ineffective Treatment Groups. | Starting from the completion of treatment and evaluation for all patients, up to 6 months | Using two-dimensional liquid chromatography to separate proteins and perform data independent acquisition (DIA) for tissue and macro proteome analysis. Qualitative and quantitative analysis of DIA data was performed using data analysis software Spectronaut, and statistical algorithms were used to obtain differentially expressed proteins in HCC tissue and macro proteome between the effective and ineffective treatment groups. At the same time, non targeted metabolomics analysis methods were used to perform metabolomics analysis on serum samples from two groups of patients. |
| Model for predicting the efficacy and prognosis of advanced HCC treatment | Within six months after the completion of treatment and evaluation for all patients. | Perform pathway and biological function enrichment analysis of differentially expressed proteins through IPA, analyze signal pathways and interaction networks of different bacterial strains through Unipept software, and perform pathway and functional analysis of differential metabolites through MetaboAnalyst to further screen important biomarkers. By using machine learning methods and combining ROC curves to construct efficacy prediction and prognosis judgment models, the optimal biomarker combination is obtained, and a prediction model is established. |
Countries
China
Outcome results
None listed