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Abstract Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan–Meier plot, time‐dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five‐gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five‐gene signature using The Cancer Genome Atlas—Liver Hepatocellular Carcinoma (TCGA‐LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five‐gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA‐LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high‐risk group and histidine metabolism in the low‐risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real‐time PCR validation. In brief, a five‐gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five‐gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.

Abstract Reprogramming of lipid metabolism is a newly recognized hallmark of malignancy. Increased lipid uptake, storage and lipogenesis occur in a variety of cancers and contribute to rapid tumor growth. Lipids constitute the basic structure of membranes and also function as signaling molecules and energy sources. Sterol regulatory element-binding proteins (SREBPs), a family of membrane-bound transcription factors in the endoplasmic reticulum, play a central role in the regulation of lipid metabolism. Recent studies have revealed that SREBPs are highly up-regulated in various cancers and promote tumor growth. SREBP cleavage-activating protein is a key transporter in the trafficking and activation of SREBPs as well as a critical glucose sensor, thus linking glucose metabolism and de novo lipid synthesis. Targeting altered lipid metabolic pathways has become a promising anti-cancer strategy. This review summarizes recent progress in our understanding of lipid metabolism regulation in malignancy, and highlights potential molecular targets and their inhibitors for cancer treatment.