BackgroundOvarian cancer (OV) is the most lethal gynecological cancer in women. We aim to develop a generalized, individualized immune prognostic signature that can stratify and predict overall survival for ovarian cancer.MethodsThe gene expression profiles of ovarian cancer tumor tissue samples were collected from 17 public cohorts, including 2777 cases totally. Single sample gene set enrichment (ssGSEA) analysis was used for the immune genes from ImmPort database to develop an immune-based prognostic score for OV (IPSOV). The signature was trained and validated in six independent datasets (n = 519, 409, 606, 634, 415, 194).FindingsThe IPSOV significantly stratified patients into low- and high-immune risk groups in the training set and in the 5 validation sets (HR range: 1.71 [95%CI: 1.32–2.19; P = 4.04 × 10−5] to 2.86 [95%CI: 1.72–4.74; P = 4.89 × 10−5]). Further, we compared IPSOV with nine reported ovarian cancer prognostic signatures as well as the clinical characteristics including stage, grade and debulking status. The IPSOV achieved the highest mean C-index (0.625) compared with the other signatures (0.516 to 0.602) and clinical characteristics (0.555 to 0.583). Further, we integrated IPSOV with stage, grade and debulking, which showed improved prognostic accuracy than clinical characteristics only.InterpretationThe proposed clinical-immune signature is a promising biomarker for estimating overall survival in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility.FundThis work was supported by National Key Program of China, and Natural Science Foundation of the Jiangsu Higher Education Institutions of China.

BackgroundDNA methylation has started a recent revolution in genomics biology by identifying key biomarkers for multiple cancers, including oral squamous cell carcinoma (OSCC), the most common head and neck squamous cell carcinoma.MethodsA multi-stage screening strategy was used to identify DNA-methylation-based signatures for OSCC prognosis. We used The Cancer Genome Atlas (TCGA) data as training set which were validated in two independent datasets from Gene Expression Omnibus (GEO). The correlation between DNA methylation and corresponding gene expression and the prognostic value of the gene expression were explored as well.ResultsThe seven DNA methylation CpG sites were identified which were significantly associated with OSCC overall survival. Prognostic signature, a weighted linear combination of the seven CpG sites, successfully distinguished the overall survival of OSCC patients and had a moderate predictive ability for survival [training set: hazard ratio (HR) = 3.23, P = 5.52 × 10−10, area under the curve (AUC) = 0.76; validation set 1: HR = 2.79, P = 0.010, AUC = 0.67; validation set 2: HR = 3.69, P = 0.011, AUC = 0.66]. Stratification analysis by human papillomavirus status, clinical stage, age, gender, smoking status, and grade retained statistical significance. Expression of genes corresponding to candidate CpG sites (AJAP1, SHANK2, FOXA2, MT1A, ZNF570, HOXC4, and HOXB4) was also significantly associated with patient’s survival. Signature integrating of DNA methylation, gene expression, and clinical information showed a superior ability for prognostic prediction (AUC = 0.78).ConclusionPrognostic signature integrated of DNA methylation, gene expression, and clinical information provides a better prognostic prediction value for OSCC patients than that with clinical information only.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-017-0392-9) contains supplementary material, which is available to authorized users.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer and displays divergent clinical outcomes. Prognostic biomarkers might improve risk stratification and survival prediction. We aimed to investigate the prognostic genes associated with overall survival. A two-step gene selection method was used to develop a seven-gene-based prognostic model based on the training set collected from The Cancer Genome Atlas (TCGA). In addition, the prognostic model was validated in an independent testing set from Gene Expression Omnibus (GEO). The score based on the model successfully distinguished HNSCC survival into high-risk and low-risk groups in the training set (HR, 2.79; 95% CI, 1.98–3.92; P=4.05×10−9) and the testing set (HR, 2.05; 95% CI, 1.35–3.11; P=7.98×10−4). In addition, the score could significantly predict 5-year survival by ROC curves (AUCs for training set, 0.73; testing set, 0.66). Combining risk scores with clinical characteristics improved the AUCs beyond using clinical characteristics alone (training set, from 0.57 to 0.75; testing set, from 0.63 to 0.72). A subgroup sensitivity analysis with HPV status and tumor sites revealed that the risk score was significant in all subgroups except oral cavity tumors of the testing set. Furthermore, HPV-positive status improves survival in oropharyngeal HNSCC but not non-oropharyngeal HNSCC. In conclusion, the seven-gene prognostic signature is a reliable and practical prognostic tool for HNSCC. This approach can add prognostic value to clinical characteristics and provides a new possibility for individualized treatment.

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