Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Development of a nomogram model to predict survival outcomes in patients with primary hepatic neuroendocrine tumors based on SEER database.

BACKGROUND: Primary hepatic neuroendocrine tumors (PH-NETs) are extremely rare and unknown. Because of its rarity, its prognosis features and influencing factors are not well established. METHODS: Data of 140 patients with PH-NETs diagnosed in the SEER database from 1975 to 2016 were collected. The demographics and clinic-pathological features were described. By using propensity-score matching (PSM) analysis, three associated cohorts were selected to describe the malignancy of PH-NETs and univariate analysis was conducted. Then, multivariate Cox analyses were performed and a predicting nomograph was constructed. C-index, receiver operating characteristic (ROC) curve and calibration curves were used to evaluate the predictive value of nomogram. RESULTS: The overall survival outcomes of PH-NETs were superior to hepatocellular carcinoma (HCC) with a mean survival time 30.64 vs 25.11 months (p = 0.052), but inferior to gastrointestinal tract neuroendocrine tumors in situ (GI-NETs in situ) with a mean survival time 30.64 vs 41.62 months (p = 0.017). With reference to gastrointestinal neuroendocrine tumors with liver metastasis (GI-NETs-LM), GI-NETs-LM had better outcomes in short time (1-year survival rate: 64.75% vs 56.43%) but was worse in long time (5-year survival rate: 8. 63% vs 18.57%). Multivariate Cox analyses showed that tumor grade and surgery were two independent factors for prognosis of the patients (p < 0.00). Tumor grade and surgery were used to construct the predicting nomogram. The C-index was 0.79 (95%CI = 0.75-0.83). The area under curve (AUC) values in ROC were 0.868 in 1-year and 0.917 in 3-year survival and the calibration curves showed good consistency. CONCLUSIONS: The overall prognosis PH-NETs is generally favorable, better than HCC and GI-NETs-LM in long term. Preoperative biopsy and complete pathological diagnosis were recommended. Radical surgical intervention including transplantation was the first choice in PH-NETs therapy.