Background: Combined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA.Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis. Methods: One hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule. Results: A nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction. Conclusions:The proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

Introduction: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in the progression of hepatocellular carcinoma (HCC). However, how their interaction is involved in the prognostic value of HCC and immune checkpoint inhibitors (ICIs) therapy remains unclear. Methods: The RNA sequencing and clinical data of HCC patients were collected from TCGA database. The prognostic m6A-related lncRNAs were screened out with Pearson correlation test, univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Patients with HCC were classified into 2 subtypes by consensus clustering. Survival analyses were performed to assess the prognostic value of different clusters and risk models. Potential tumor correlated biological pathways correlated with different clusters were explored through gene set enrichment analysis. We also identified the relationship of the risk model and clusters with response to immune checkpoint inhibitors (ICIs) therapy and tumor microenvironment (TME). Furthermore, the prognostic value of the 9 m6A-related lncRNAs was validated in the external cohort. Finally, the role of SNHG4 was explored by silencing and overexpression of SNHG4 through conducting proliferation, migration and invasion experiments. Results: Patients from 2 clusters and different risk groups based on m6A-related lncRNAs had significantly different clinicopathological characteristics and overall survival outcomes. Tumor-correlated biological pathways were found to be correlated with Cluster 2 through GSEA. Moreover, we found that patients from different clusters and risk groups expressed higher levels of immune checkpoint genes and had distinct TME and different responses for ICIs therapy. Prognostic value of this risk model was further confirmed in the external cohort. Finally, consistent with the discovery, SNHG4 played an oncogenic role in vitro. Conclusion:Our study demonstrated that the 9 m6A-related lncRNA signature may serve as a novel predictor in the prognosis of HCC and optimize (ICIs) therapy. SNHG4 plays an oncogenic role in HCC.

BackgroundIntrahepatic cholangiocarcinoma (ICC) is the second most common primary liver cancer with increasing incidence in the last decades. Microvascular invasion (MVI) is a poor prognostic factor for patients with ICC, which correlates early recurrence and poor prognosis, and it can affect the selection of personalized therapeutic regime.PurposeThis study aimed to develop and validate a radiomics-based nomogram for predicting MVI in ICC patients preoperatively.MethodsA total of 163 pathologically confirmed ICC patients (training cohort: n = 130; validation cohort: n = 33) with postoperative Ga-DTPA-enhanced MR examination were enrolled, and a time-independent test cohort (n = 24) was collected for external validation. Univariate and multivariate analyses were used to determine the independent predictors of MVI status, which were then incorporated into the MVI prediction nomogram. Least absolute shrinkage and selection operator logistic regression was performed to select optimal features and construct radiomics models. The prediction performances of models were assessed by receiver operating characteristic (ROC) curve analysis. The performance of the MVI prediction nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsLarger tumor size (p = 0.003) and intrahepatic duct dilatation (p = 0.002) are independent predictors of MVI. The final radiomics model shows desirable and stable prediction performance in the training cohort (AUC = 0.950), validation cohort (AUC = 0.883), and test cohort (AUC = 0.812). The MVI prediction nomogram incorporates tumor size, intrahepatic duct dilatation, and the final radiomics model and achieves excellent predictive efficacy in training cohort (AUC = 0.953), validation cohort (AUC = 0.861), and test cohort (AUC = 0.819), fitting well in calibration curves (p > 0.05). Decision curve and clinical impact curve further confirm the clinical usefulness of the nomogram.ConclusionThe nomogram incorporating tumor size, intrahepatic duct dilatation, and the final radiomics model is a potential biomarker for preoperative prediction of the MVI status in ICC patients.