Prediction of Prognosis for cHCC-CC Patients After Surgery: Comparison of Tumor Marker Score Based on AFP, CEA, CA19-9, and Other Clinical Stages.

BACKGROUND: The effectiveness of clinical stage as a prognostic factor in combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) patients is controversial. PATIENTS AND METHODS: Medical records of all pathologically confirmed cHCC-CC patients from 2000 to 2017 at West China Hospital were retrieved. Tumor marker score (TMS) was determined from optimal AFP, CEA, and CA19-9 cutoff values. Interaction and subgroup analysis were conducted according to potential confounders. Prognostic value of TMS and other prognostic models were evaluated by Kaplan-Meier (K-M) analysis, c-index, and time-dependent receiver operating curves (td-ROC). RESULTS: Optimal cutoff values for preoperative AFP, CEA, and CA19-9 were 10.76 ng/mL, 5.24 ng/mL, and 31.54 U/mL, respectively. Among 128 patients, 24, 58, and 46 were classified into TMS 0, TMS 1, and TMS ≥ 2, respectively. TMS could stratify our series into groups of statistically different prognosis. Subgroup analysis according to potential confounders and test for interactions showed that TMS 1 and TMS ≥ 2 were stable risk factors relative to TMS 0. Univariate (HR: TMS1 = 2.30, p = 0.014; TMS ≥ 2 = 5.1, p < 0.001) and multivariate Cox regression analyses (HR: TMS1 = 1.72, p = 0.124; TMS ≥ 2 = 4.15, p < 0.001) identified TMS as an independent prognostic risk factor. TMS had good discrimination (c-index 0.666, 95% CI 0.619-0.714), and calibration plots revealed favorable consistency. Area under the curve (AUC) value of td-ROC for TMS and integrated AUC was higher than for other clinical stages at any month within 5 years postoperation. CONCLUSION: TMS exhibited optimal prognostic value over other widely used clinical stages for cHCC-CC after surgery and may guide clinicians in prognostic prediction.

[Interference of Lumican Regulates the Invasion and Migration of Liver Cancer Cells].

OBJECTIVE: To explore the effect of silencing lumican on the invasion and migration of liver cancer cells. METHODS: Lumican was silenced by shRNA in liver cancer cells (HepG2 and MHCC97H). The mRNA levels of lumican were detected by qRT-PCR. Cell invasion was measured by Transwell. Cell migration was tested via wound healing. The protein levels of lumican,MMP-9,VEGF,ERK1,JNK,p-ERK1 and p-JNK were measured by Western blot. RESULTS: Liver cancer cells (HepG2 and MHCC97H) had higher levels of mRNA and protein of lumican compared with normal hepatocyte L02 (P<0.01). shRNA lowered the levels of mRNA and protein of lumican (P<0.01),and weakened the invasion and migration of cancer cells (P<0.01). The expressions of MMP-9 and VEGF decreased with the shRNA silence (P<0.01). shRNA also reduced the protein level of p-ERK1 and p-JNK (P<0.01). CONCLUSION: Silencing lumican by shRNA attenuates the invasion and migration of liver cancer cells via inhibiting the activation of ERK1/JNK pathway.

Sarcopenia predicts an adverse prognosis in patients with combined hepatocellular carcinoma and cholangiocarcinoma after surgery.

BACKGROUND: The prognostic value of sarcopenia in combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) patients after surgery has not been evaluated, while the efficacy of the available tumor stage for cHCC-CC remains controversial. METHODS: All consecutive cHCC-CC patients after surgery were retrieved. The patients were stratified by the sex-specific medians of the psoas muscle index into groups with or without sarcopenia. Prognosis was analyzed using the Kaplan-Meier (K-M) method, and the K-M curves were adjusted by inverse probability weighting (IPW). A nomogram based on Cox regression analysis was established and further compared with primary liver cancer (PLC) stages by internal validation based on bootstrap resampling and k-fold cross-validation. RESULTS: A total of 153 patients were stratified into sarcopenia and non-sarcopenia groups. The sarcopenia group revealed statistically worse overall survival (OS) and disease-free survival (DFS) using the K-M method and K-M curves adjusted by IPW. Multivariate Cox regression analyses suggested sarcopenia as an independent risk factor for OS (HR = 1.55; p = 0.040) and DFS (HR = 1.55; p = 0.019). Subgroup analysis based on baseline variables showed sarcopenia as a stable risk factor for the prognosis. Our nomogram outperformed PLC stages in prognostic prediction, as evidenced by the best c-index, area under the curve, and positive improvement of the net reclassification index and integrated discrimination improvement. A fivefold cross-validation revealed consistent results. Decision curve analysis revealed higher net benefits of the nomogram than PLC stages. CONCLUSIONS: Sarcopenia is an independent and stable risk factor for the prognosis of cHCC-CC patients after surgery. Our nomogram might aid high-risk patient identification and clinical decisions.

Nomogram Models for Predicting Risk and Prognosis of Newly Diagnosed Ovarian Cancer Patients with Liver Metastases - A Large Population-Based Real-World Study.

Background: Previous studies about liver metastases (LM) in newly diagnosed ovarian cancer (NDOC) patients based on Surveillance, Epidemiology, and End Results (SEER) program disregarded selection bias of missing data. Methods: We identified Data of NDOC patients from SEER between 2010 and 2016, presented a comprehensive description of this dataset, and limited possible biases due to missing data by applying multiple imputation (MI). We determined predictive factors for underlying LM development in NDOC patients and evaluated prognostic factors in NDOC patients with LM (OCLM). We then established predictive nomograms, assessed by the concordance index, calibration curve, decision curve analysis (DCA), and clinical impact curves (CIC). Results: The amount of missing data for different variables in SEER dataset ranges from 0 to 36.11%. The results between complete dataset and MI datasets are similar. LM prevalence in NDOC patients was 7.18%, and median overall survival for OCLM patients was 11 months. The C-index of risk nomogram for LM development in the training cohort (TC) and validation cohort (VC) were 0.764 and 0.759, respectively. The C-index and integrated area under curve within five years of prognostic nomogram for OCLM patients in the TC and VC were 0.743 and 0.773, 0.714 and 0.733, respectively. For both nomograms, DCA revealed favorable clinical use and calibration curves suggested good consistency. Conclusion: The risk nomogram is expected to aid clinicians in identifying high-risk groups of LM development in NDOC patients for intensive screening. The prognostic nomogram could facilitate individualized prediction and stratification for clinical trials in OCLM patients.