A prognostic 11 genes expression model for ovarian cancer.

The symptoms of ovarian cancer at early stages are usually absent which makes the diagnosis in its early stages exceedingly difficult. Previous research has proven that ovarian cancer is a genetic disease, which depends on the alteration of multi-cancer related genes and anti-cancer genes, multi-stages and multi-pathways, involving a variety of oncogene activation and anti-oncogene inactivation. For a better understanding of the prognostic classification of ovarian cancer, gene expression profiles were used to analyze the prognostic factors of ovarian cancer, and the prognostic model was used to classify the ovarian cancer samples. The ovarian cancer samples data were downloaded from TCGA dataset. Rebust likelihood-based survival model was built to find the key genes that could function as prognostic markers. The samples were classified by unsupervised hierarchical clustering. Furthermore, Kaplan-Meier survival analysis was used to analyze the differences in the prognosis of the samples. The prognostic model was used to classify the samples, and then the best classification model was selected as the prognostic model of ovarian cancer. Finally, GEO datasets were used for external data validation. A total of 886 genes with influence on prognosis was obtained. Then genomic combinations of 11 genes were screened out by random sampling. Then the active number of influential factors was counted based on the expression level of featured genes. When the number of influencing factors is ≥7, the prognosis difference among these genes is the largest (P-value = 0.000775); and this was chosen as the final Classification model. To summary, a prognostic 11genes expression model was preliminarily built to classify the ovarian cancer samples.

A 15-long non-coding RNA signature to improve prognosis prediction of cervical squamous cell carcinoma.

Long non-coding RNAs (lncRNAs), which have little or no protein-coding capacity, caught a particular interest since their potential roles in the cancer paradigm. As the most common cancer in women, cervical squamous cell carcinoma remains one of the leading causes of deaths from cancer. However, limited evidence is available to determine the role of lncRNAs in the prognosis of cervical squamous cell carcinoma. In this study, we collected lncRNA expression profiling to identify prognosis related lncRNAs for cervical squamous cell carcinoma from TCGA database. In addition, we developed a 15-lncRNA signature based risk score to comprehensively assess the prognostic function of lncRNA. Furthermore, we performed a ROC analysis to identify the optimal cut-off point for classification risk level of the patients. Univariate Cox regression models were used to assess the association between lncRNAs and prognosis of patients with cervical squamous cell carcinoma. A 15-lncRNA based risk score was developed based on the Cox co-efficient of the individual lncRNAs. The prognostic value of this risk score was validated in the complete set and internal testing set. In summary, a 15-lncRNA expression signature (BAIAP2-AS1, RP11-203J24.8, LINC01133, RP1-7G5.6, RP11-147L13.15, SERHL, CTC-537E7.3, RP11-440L14.1, RP11-131N11.4, ILF3-AS1, RP11-80H18.4, RP11-1096G20.5, CTD-2192J16.26, RP11-621L6.3, and RP11-571M6.18) were identified and validated which can predict cervical cancer patient survival. The potential functions of this 15-lncRNA expression signature and individual lncRNAs as prognostic targets of cervical cancer were revealed by this study. Furthermore, these findings may have important implications in the understanding of the potential therapeutic method for the cervical squamous cell carcinoma patients.

SET and MYND Domain-Containing Protein 3 (SMYD3) Polymorphism as a Risk Factor for Susceptibility and Poor Prognosis in Ovarian Cancer.

BACKGROUND We investigated the relationship of the polymorphisms of SET and MYND domain-containing protein 3 (SMYD3) with risk and prognosis of ovarian cancer. MATERIAL AND METHODS The polymerase chain reaction (PCR) amplification method was applied to detect the polymorphisms of variable number of tandem repeats (VNTR) in the SMYD3 gene promoter region for 156 patients with ovarian cancer (case group) and 174 healthy people (control group). Quantitative reverse transcription polymerase chain reaction and Western blot were applied to detect SMYD3 mRNA and protein expressions. RESULTS The frequencies of VNTR genotype 3/3 and allele genotype 3 in the case group were significantly higher than those in the control group, while the frequency of genotype 2/2 in the control group was significantly higher than that in case group (all P<0.05). The proportion of poorly differentiated patients carrying VNTR genotype 3/3 was significantly higher than the proportion of poorly differentiated patients carrying VNTR genotype 2/2+2/3, while the proportion of patients carrying genotype 3/3 with International Federation of Gynecology and Obstetrics (FIGO) stage III-IV disease was significantly higher than the proportion of patients carrying genotype 2/2 +2/3 with FIGO stage III-IV disease (all P<0.05). SMYD3 mRNA and protein expressions were higher in the patients carrying genotype 3/3 than they were in the patients with the 2/2+2/3 genotype (all P<0.05). The 5-year survival rate for patients carrying VNTR genotype 3/3 was significantly lower than that of patients carrying genotype 2/2+2/3, and Cox regression analysis showed that VNTR genotype 3/3 was an independent risk factor for ovarian cancer prognosis (all P<0.05). CONCLUSIONS VNTR genotype 3/3 of the SMYD3 gene was associated with the risk of ovarian cancer. The polymorphism of VNTR genotype could be recognized as an indicator for the poor prognosis of patients with ovarian cancer.