Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis, an iron-dependent form of regulated cell death, can be induced by sorafenib. However, the prognostic value of ferroptosis-related genes in HCC remains to be further elucidated. In this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Our results showed that most of the ferroptosis-related genes (81.7%) were differentially expressed between HCC and adjacent normal tissues in the TCGA cohort. Twenty-six differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 10-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001 in the TCGA cohort and P = 0.001 in the ICGC cohort). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups. In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in HCC. Targeting ferroptosis may be a therapeutic alternative for HCC.

The interaction between hypoxia and immune status has been confirmed in various cancer settings, and corresponding treatments have been investigated. However, reliable biomarkers are needed for individual treatment, so we sought to develop a novel scoring system based on hypoxia and immune status. Prognostic hypoxia-immune status-related signatures of patients with triple-negative breast cancer (TNBC) were identified in The Cancer Genome Atlas (TCGA) (N = 158), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), and GSE58812 (N = 107). LASSO Cox regression was used for model construction. Hypoxia and immune status expression profiles were analyzed, and infiltrating immune cells were compared. Quantitative real-time PCR (qRT-PCR) was used for validation in the Sun Yat-sen University Cancer Center (SYSUCC) cohort, and immunofluorescence was applied for the detection of hypoxia and immune markers in cancer tissues. Ten cross-cohort prognostic hypoxia-immune signatures were included to construct the comprehensive index of hypoxia and immune (CIHI) in the METABRIC cohort. Two subgroups of patients with distinct hypoxia-immune status conditions were identified using CIHI: hypoxia high /immune low and hypoxia low /immune high , with a significantly better overall survival (OS) rate in the latter (P < 0.01). The prognostic value of CIHI was further validated in the TCGA, GSE58812, and SYSUCC cohorts (P < 0.01).

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Our study aims to construct a prognosis-related immune phenotype classifier for predicting clinical prognosis and immune activity in triple-negative breast cancer (TNBC). A total of 237 patients with TNBC from Sun Yat-sen University Cancer Center (SYSUCC) and 533 patients with TNBC from public datasets were included in our study. A stromal immune quantified index was generated with a LASSO Cox regression model based on five prognosis-related immune cells evaluated by CIBERSORT or IHC and was used to determine immune phenotypes. Immune features were evaluated in the samples before chemotherapy. A total of 119 patients in the SYSUCC training cohort were classified into immune Phenotypes A and B according to the density of stromal CD4+ T cells, γδ T cells, monocytes, M1 macrophages and M2 macrophages. Phenotype A predicted better survival than Phenotype B, and the classification was further validated in the testing cohort of 118 patients and the validation cohort of 533 patients. In the combined cohort, significant differences were found in Phenotype A compared to Phenotype B for the 5-year overall survival (83.5% vs 65.8%, respectively, P < .01) and the 5-year disease-free survival (87.3% vs 76.0%, respectively, P < .01). In Phenotype A, immune-related pathways were significantly enriched, and a higher level of immune checkpoint molecules, including PD-L1, PD-1 and CTLA-4, could be observed. The immune phenotype classification was an independent prognostic indicator for TNBC and might serve as a potential predictor for immune activity within the tumor microenvironment. K E Y W O R D Sclassifier, immune phenotype, prognosis, stromal immune score, triple-negative breast cancer