Background: Ovarian squamous cell carcinoma (SCC) is a rare cancer with possible poor survival, however no direct evidence supports this viewpoint and the independent prognostic factors are controversial. Patients with ovarian SCC and serous carcinoma (SC) who were diagnosed between 2004 and 2016 were selected using the recent released SEER database. Propensity score matching was used to balance the characteristics of the two groups. The difference of survival between patients with ovarian SCC and SC was explored using Kaplan-Meier method. Cox regression analyses were performed to further identify the independent prognostic factors among patients with ovarian SCC. Results: Of 15,286 patients (15,106 SC cases and 180 SCC cases), 304 were identified in the matched cohort (200 SC cases and 104 SCC cases). The overall survival and cause-specific survival for patients with SCC were significantly poorer (P_log-rank < 0.001). The median survival time was 21 months for SCC and 95 months for SC. Patients who underwent bilateral salpingo-oophorectomy with hysterectomy and omentectomy seemed to have a better outcome. In multivariate analysis, older age at diagnosis, larger tumor size, bilateral and FIGO stage IV malignancy were the independent risk factors for poor disease outcome. Conclusions: The prognosis of ovarian SCC is worse than ovarian SC. Older age at diagnosis, advanced disease stage, larger tumor size and bilateral malignancy are the independent risk factors for poor survival.

Gao W, et al. J Gastric Cancer. 2019 Sep;20:e18. https://doi.org/10.5230/jgc.2020.20.e18

Background: To investigate the prognostic significance of the cumulative score based on preoperative fibrinogen and pre-albumin (FP score) in patients with gastric cancer after radical gastrectomy.

Zhang C, et al. J Gastric Cancer. 2022 Apr;22(2):120-134. https://doi.org/10.5230/jgc.2022.22.e12

Tumor metastasis is a common event in patients with gastric cancer (GC) who previously underwent curative gastrectomy. It is meaningful to employ high-volume clinical data for predicting the survival of metastatic GC patients. We aim to establish an improved machine learning (ML) classifier for predicting if a patient with metastatic GC would die within 12 months. Eligible patients were enrolled from a Chinese GC cohort, and the complete detailed information from medical records was extracted to generate a high-dimensional dataset. Appropriate feature engineering and feature filter were conducted before modeling with eight algorithms. A 10-fold cross validation (CV) nested in a holdout CV (8:2) was employed for hyperparameter tuning and model evaluation. Model selection was based on the area under the receiver operating characteristic (AUROC) curve, recall, and precision. The selected model was globally explained using interpretable surrogate models. Of the total 399 cases (median survival of 8.2 months), 242 patients survived less than 12 months. The linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) model had the highest AUROC (0.78 ± 0.021), recall (0.93 ± 0.031), and precision (0.80 ± 0.026), respectively. The LDA model created a new function that generally separated the two classes. The predicted probability of the SVM model was interpreted using a linear regression model visualized by a nomogram. The predicted class of the RF model was explained using a decision tree model. In summary, analyzing high-volume medical data by ML is helpful to produce an improved model for predicting the survival in patients with metastatic GC. The algorithm should be carefully selected in different practical scenarios.

Background/Aims: It has been proposed that hepatitis delta virus (HDV) induces hepatic carcinogenesis by distinct molecular events compared with hepatocellular carcinoma (HCC) that is commonly induced by other hepatitis virus. This study aimed to explore underlying mechanism by identifying the key genes for HDV-HCC using bioinformatics analysis. Materials and Methods: The GSE107170 dataset was downloaded and the differentially expressed genes (DEGs) were obtained by the online tool GEO2R. Gene otology (GO) functional analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R packages. The protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Hub genes were selected by Cytoscape software according to degree algorithm. The hub genes were further validated in terms of expression and survival analysis based on public databases. Results: A total of 93 commonly up-regulated genes and 36 commonly down-regulated genes were found. The top 5 up-regulated hub genes were TFRC, ACTR2, ARPC1A, ARPC3 and ARPC2. The top 5 down-regulated hub genes were CTNNB1, CCND1, CDKN1B, CDK4 and CDKN1A. In the validation analysis, the expressions of ARPC1A, ARPC3 and CDK4 were promoted in general liver cancer samples. Higher expressions of ARPC2 and CDK4 and lower expressions of CDKN1A, CCND1 and CDKN1B were associated with worse prognosis in general HCC patients. Conclusion: The present study identifies a series of key genes that may be involved in the carcinogenesis of HDV-HCC and used as prognostic factors.