Investigation people
Inside the expose studies, we seemed and you can installed mRNA term processor study of HCC architecture from the GEO database utilising the words out-of “hepatocellular carcinoma” and you can “Homo sapiens”. Half dozen microarray datasets (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and you may GSE14520 (in line with the GPL571 system) have been acquired to own DEGs investigation. Details of the brand new GEO datasets utilized in this research receive when you look at the Table 1. RNA-sequencing data off 371 HCC tissues and 50 regular frameworks stabilized from the log2 conversion process was basically acquired regarding the Cancer Genome Atlas (TCGA) getting viewing new provided DEGs on half dozen GEO datasets and building gene prognostic models. GSE14520 datasets (according to the GPL3921 platform) included 216 HCC frameworks having complete clinical advice and you may mRNA expression analysis to have external recognition of prognostic gene signature. Just after leaving out TCGA cases which have partial scientific pointers, 233 HCC customers using their done years, sex, gender, tumor stages, Western Combined Committee into the Disease (AJCC) pathologic cyst stage, vascular intrusion, Os status and you may big date advice was indeed integrated to own univariable and you may multivariable Cox regression studies. Mutation investigation was extracted from the brand new cBioPortal to have Disease Genomics .
Processing away from gene phrase studies
To integrated gene expression chip data downloaded from the GEO datasets, we firstly conducted background correction, quartile normalization for the raw data followed by log2 transformation to obtain normally distributed expression values. The DEGs between HCC tissues and non-tumor tissues were identified using the “Limma” package in R . The thresholds of absolute value of the log2 fold change (logFC) > 1 and adjusted P value < 0.05 were adopted. Mean expression values were applied for genes with multiprobes. Then, we used the robust rank aggregation (RRA) method to finally identify overlapping DEGs (P < 0.05) from the six GEO datasets.
Framework away from a potential prognostic signature
To identify the prognostic genes, we firstly sifted 341 patients from the TCGA Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort with follow-up times of more than 30 days. Then, univariable Cox regression survival analysis was performed based on the overlapping DEGs. A value of P < 0.01 in the univariable Cox regression analysis was considered statistically significant. Subsequently, the prognostic gene signature was constructed by Lasso?penalized Cox regression analysis , and the optimal values of the penalty parameter alpha were determined through 10-times cross-validations by using R package “glmnet” . Based on the optimal alpha value, a twelve-gene prognostic signature with corresponding coefficients was selected, and a risk score was calculated for each TCGA-LIHC patient. Next, the HCC patients were divided into two or three groups based on the optimal cutoff of the risk score determined by “survminer” package in R and X-Tile software. To assess the performance of the twelve-gene prognostic signature, the Kaplan–Meier estimator curves and the C-index comparing the predicted and observed OS were calculated using package “survival” in R. Time-dependent receiver operating characteristic (ROC) curve analysis was also conducted by using the R packages “pROC” and “survivalROC” . Then, the GSE14520 datasets with complete clinical information was used to validate the prognostic performance of twelve-gene signature. The GSE14520 external validation datasets was based on the GPL3921 platform of the Affymetrix HT Human Genome U133A Array Plate Set (HT_HG-U133A, Affymetrix, Santa Clara, CA, United States).
The risk score and other clinical variants, including age, body mass index (BMI), sex, tumor grade, the AJCC pathologic tumor stage, vascular invasion, residual tumor status and AFP value, were analyzed by univariable Cox regression analysis. Next, we conducted a multivariable Cox regression model that combined the risk sites de rencontres fétiches du pied score and the above clinical indicators (P value < 0.2) to assess the predictive performance. The univariable and multivariable Cox regression analysis were performed with TCGA-LIHC patients (n = 234) that had complete clinical information.