bioRxivBiochemistryPreprint
Proteogenomic characterization reveals antitumor mechanisms of intrahepatic cholangiocarcinoma
Abstract
Diagnosing and managing intrahepatic cholangiocarcinoma (iCCA) poses a significant oncological hurdle as it is a deadly form of liver cancer.A significant role is played by nonapoptotic regulatory cell death (NRCD) in the tumor immune microenvironment (TME) of iCCA. Nevertheless, the precise functions of NRCD-associated genes (NRGs) in the tumor microenvironment (TME) are still not well understood. Transcriptomics, proteomic analysis, and single-cell RNA analysis were utilized to distinguish two NRG-related clusters in iCCA patients from the FU-iCCA cohort in this study. We have shown the clear disparities in immune traits and predictive categorization among two groups. To address the risk stratification and prognosis prediction, a prognostic signature model called NPS (NRG-related risks core prognostic signature model) was created using the FU-iCCA cohort. The validation of the NRG-associated risk score in prognosis and immunotherapy confirmed its predictive capabilities. In iCCA patients with high-risk, the secretion of CP and TGF-{beta} proteins strengthened the enriched TGF-{beta} signaling network between CD4+ T cells and erythroid cells. Patients exhibiting a low-risk score demonstrated improvement in the effector function of CD4+ T cells, resulting in a more favorable reaction to chemotherapy medications. The NPS-risk score showed a significant negative correlation with the IC50 values of four drugs (Trametinib, CI-1040, X17-ACC, and PD-0325901). From these findings, it can be inferred that a clear connection exists between the NRG and the TME in iCCA. Additionally, the risk score has the potential to act as a reliable prognostic indicator, offering advantageous outcomes for chemotherapy and immunotherapy. This could aid in making informed clinical decisions for patients with iCCA.
- organism: human
- assay: proteomics
- assay: bulk RNA-seq
- assay: multiome
- research focus: protein structure prediction
- research focus: protein design
- approach: statistical model
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