To quantitatively analyze lipid molecules in tumors and adjacent tissues of intrahepatic cholangiocarcinoma (ICC), to establish diagnostic model and to examine lipid changes with clinical classification. Patients and Methods: We measured the quantity of 202 lipid molecules in 100 tumor observation points and 100 adjacent observation points of patients who were diagnosed with ICC. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were handles, along with Student's t-test to identify specific metabolites. Prediction accuracy was validated in the validation set. Another logistic regression model was also established on the training set and validated on the validation set. Results: Distinct separation was obtained from PCA and OPLS-DA model. Ten differentiating metabolites were identified using PCA, OPLA-DA and Lasso regression: [m/z 722.5130], [m/z 863.5655], [m/z 436.2834], [m/z 474.2626], [m/z 661.4813], [m/z 750.5443], [m/z 571.2889], [m/z 836.5420], [m/z 772.5862] and [m/z 478.2939]. Using logical regression, a diagnostic equation: y = 3.4*[m/z 436.2834] -3.773*[m/z 474.2626] + 3.82*[m/z 661.4813] -4.394*[m/z 863.5655] + 10.165 based on four metabolites successfully differentiated cancerous areas from adjacent normal areas. The AUROC of the model reached 0.993 (95% CI: 0.985-0.999) in the validation group. Compared with the adjacent non-tumor area, three characteristic metabolites FA (22:4), PA (P-18:0/0:0) and Glucosylceramide (d18:1/12:0) showed an increasing trend from stage I to stage II, while seven other metabolites LPA(16:0), PE (34:2), PE(36:4), PE(38:3), PE(40:6), PE(40:5) and LPE(16:0) showed a decreasing trend from stage I to stage II. Conclusion: We successfully identified lipid molecules in differentiating tumor tissue and adjacent tissue of ICC, established a discrimination logistic model which could be used as a classifier to classify tumor and non-tumor regions based on analysis in tumor margins and provided information for biomarker changes in ICC, and proposed to related lipid changes with clinical classification.
Brain metastasis from intrahepatic cholangiocarcinoma (iCCA) is extremely rare, and no standard therapeutic strategy has been established. Camrelizumab is a programmed cell death protein 1 (PD-1) inhibitor that has been widely studied in treating liver cancer. Combined immunotherapy and targeted therapy are a promising approach for treating advanced iCCA. Despite that immune checkpoint inhibitor (ICI)-based neoadjuvant therapy on iCCA has shown a significant response rate and resection rate, few reports have shown the therapeutic efficacy of immunotherapy in treating brain metastasis from iCCA. Although PD-1 inhibitors such as pembrolizumab, nivolumab, or camrelizumab are increasingly applied in clinic practice to treat multiple malignancies, to the best of our knowledge, we report the first case of an iCCA patient with brain metastasis successfully treated with a combined immunotherapy and targeted therapy. The patient is a 54-year-old man with metastatic iCCA in brain treated though camrelizumab plus lenvatinib therapy with a complete response (CR). By the time of writing, he has had a progression-free survival of 17.5 months and did not experience any severe side effects related to this therapy. Camrelizumab plus lenvatinib therapy showed favorable efficacy and manageable toxicity for this patient with advanced iCCA and could be of interest for more prospective randomized trials to further verify the potential clinical benefits.
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