Background: Liver function is a routine laboratory test prior to curative liver resection. It remains unclear whether the albumin-bilirubin (ALBI) grade and albumin-to-alkaline phosphatase ratio (AAPR) can predict long-term outcomes of surgically treated patients with intrahepatic cholangiocarcinoma (ICC). Methods: This study investigated the correlation between ALBI grade and AAPR with overall survival (OS) after liver resection and then compared their accuracy to the Child-Pugh score. Harrell's concordance index (C-index) and Akaike information criterion (AIC) were used to compare accuracy of models. Results: A total of 620 ICC patients were included, 477 in derivation cohort and 143 for validation. 0.348 was identified as the cutoff value for AAPR after calculating the Youden index. In the derivation cohort, elevated ALBI grade was associated with worse prognosis [hazard ratio (HR): 1.751, 95% confidence interval (CI): 1.329 to 2.306], and a decreased AAPR value was correlated with shorter OS (HR: 1.969, 95% CI: 1.552 to 2.497). Multivariate analysis suggested that the ALBI grade, AAPR, CA19-9, tumor number, and microvascular invasion were independent prognostic predictors for OS. ALBI grade and AAPR showed more accuracy in evaluating OS for surgically treated ICC patients than the Child-Pugh score (C-index: 0.559, 0.600 vs. 0.528; AIC: 3023.84, 3007.73 vs. 3034.66). Our findings were validated in an independent cohort from another clinical center. Conclusions: Importantly, the ALBI grade and AAPR showed greater discriminatory power than the Child-Pugh score in assessing long-term outcomes following hepatectomy for ICC. The AAPR was more accurate than the ALBI grade. It was beneficial to consider the ALBI grade and AAPR as useful surrogate markers to identify patients at risk of poor postoperative outcomes.

Background: The chemotherapy response score (CRS) system is a reproducible prognostic tool for patients receiving neoadjuvant chemotherapy (NACT) for tubo-ovarian high-grade serous carcinoma (HGSC). Achieving CRS 3 following NACT can be used as a surrogate for progression-free survival (PFS) and overall survival (OS). This study aimed to identify predictors of CRS 3 and develop a predictive nomogram. Methods: Data were extracted from 106 HGSC patients receiving NACT. Logistic regression was used to identify independent predictors for CRS 3. A nomogram was established based on the multivariate regression model. Results: All patients received three cycles of NACT, and CRS 3 was observed in 24 (22.6%) patients. Compared with patients in the CRS 1-2 group, patients in the CRS 3 groups had significantly improved PFS (log-rank test P < 0.0001). The multivariate regression analysis identified post-NACT CA125, percent decrease in CA125, post-NACT human epididymis protein 4 (HE4), and post-NACT hemoglobin level as independent predictors of CRS 3. The Hosmer-Lemeshow test showed goodness-of-fit of this regression model (P = 0.272). The nomogram including these factors presented good discrimination (area under the curve = 0.82), good calibration (mean absolute error = 0.039), and a net benefit within the threshold probabilities of CRS 3 > 5%. Conclusions: We validated the prognostic role of the CRS system and developed a nomogram that predicts the possibility of CRS 3 following NACT. The nomogram helps to identify patients who would benefit the most from NACT. More studies are warranted to validate this model.

The prognostic significance of tumor burden score (TBS) on patients who underwent curative-intent resection of intrahepatic cholangiocarcinoma (ICC) has not been evaluated. The present study aimed to investigate the impact of TBS and its synergistic effect with CA19-9 (combination of TBS and CA19-9, CTC grade) on long-term outcomes.

Background: This study was to explore differential RNA splicing patterns and elucidate the function of the splice variants served as prognostic biomarkers in colorectal cancer (CRC). Methods: Genome-wide profiling of prognostic alternative splicing (AS) events using RNA-seq data from The Cancer Genome Atlas (TCGA) program was conducted to evaluate the roles of seven AS patterns in 330 colorectal cancer cohort. The prognostic predictors models were assessed by integrated Cox proportional hazards regression. Based on the correlations between survival associated AS events and splicing factors, splicing networks were built. Results: A total of 2,158 survival associated AS events in CRC were identified. Interestingly, most of these top 20 survival associated AS events were adverse prognostic factors. The prognostic models were built by each type of splicing patterns, performing well for risk stratification in CRC patients. The area under curve (AUC) of receiver operating characteristic (ROC) for the combined prognostic predictors model could reach 0.963. Splicing network also suggested distinguished correlation between the expression of splicing factors and AS events in CRC patients. Conclusion: The ideal prognostic predictors model for risk stratification in CRC patients was constructed by differential splicing patterns of 13 genes. Our findings enriched knowledge about differential RNA splicing patterns and the regulation of splicing, providing generous biomarker candidates and potential targets for the treatment of CRC.

Tumor-associated macrophages (TAMs) are regarded as the most abundantly infiltrating immune cells around the tumor microenvironment (TME) in head and neck squamous cell carcinoma (HNSCC), which plays an essential role in immunosuppression and tumorigenesis. In the TCGA HNSCC cohort, 500 patients with clinical-pathological information and RNA sequence expression were randomly assigned to training for lasso regression and validation for verification, respectively. A TAM-based ten-gene signature (TBGs) was constructed, which divided the patients into high-risk and low-risk groups, could predict overall survival (OS) of HNSCC patients in the training dataset (p = 3.527e-05) and validation dataset (p = 3.785e-02). The result of Cox univariate and multivariate regression analyses showed that the risk score of TBGs could be an independent prognostic factor in HNSCC. ROC curve confirmed that the risk score of TBGs has good sensitivity and specificity for prognosis prediction (AUC = 0.659) and was also verified by the validation dataset (AUC = 0.621). We obtained key risk transcription factors (TFs)-EHF and SNAI2-by correlation analysis with TBGs. Moreover, we ran a gene set enrichment analysis (GSEA) to speculate that TBGs act on interstitial remodeling, tumor killing, metabolic reprogramming, and tumor immune-related pathways. Finally, we combined clinical-pathological features and risk score of TBGs to establish clinical nomograms, and calibration curves verified the accuracy of long-term clinical prognosis in the two datasets (C-index of 5-year OS = 0.721 and 0.716). In general, the TBGs we obtained may accurately predict the prognosis of HNSCC patients to provide personalized treatment.