Dataset Information

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer.

ABSTRACT: Simultaneous identification of multiple single genes and multi-gene prognostic signatures with higher efficacy in liver cancer has rarely been reported. Here, 1,173 genes potentially related to the liver cancer prognosis were mined with Coremine, and the gene expression and survival data in 370 samples for overall survival (OS) and 319 samples for disease-free survival (DFS) were retrieved from The Cancer Genome Atlas. Numerous survival analyses results revealed that 39 genes and 28 genes significantly associated with DFS and OS in liver cancer, including 18 and 12 novel genes that have not been systematically reported in relation to the liver cancer prognosis, respectively. Next, totally 9,139 three-gene combinations (including 816 constructed by 18 novel genes) for predicting DFS and 3,276 three-gene combinations (including 220 constructed by 12 novel genes) for predicting OS were constructed based on the above genes, and the top 15 of these four parts three-gene combinations were selected and shown. Moreover, a huge difference between high and low expression group of these three-gene combination was detected, with median survival difference of DFS up to 65.01 months, and of OS up to 83.57 months. The high or low expression group of these three-gene combinations can predict the longest prognosis of DFS and OS is 71.91 months and 102.66 months, and the shortest is 6.24 months and 13.96 months. Quantitative real-time polymerase chain reaction and immunohistochemistry reconfirmed that three genes F2, GOT2, and TRPV1 contained in one of the above combinations, are significantly dysregulated in liver cancer tissues, low expression of F2, GOT2, and TRPV1 is associated with poor prognosis in liver cancer. Overall, we discovered a few novel single genes and multi-gene combinations biomarkers that are closely related to the long-term prognosis of liver cancer, and they can be potential therapeutic targets for liver cancer.

SUBMITTER: Liu M

PROVIDER: S-EPMC7270198 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer.

Liu Meiliang M Liu Xia X Liu Shun S Xiao Feifei F Guo Erna E Qin Xiaoling X Wu Liuyu L Liang Qiuli Q Liang Zerui Z Li Kehua K Zhang Di D Yang Yu Y Luo Xingxi X Lei Lei L Tan Jennifer Hui Juan JHJ Yin Fuqiang F Zeng Xiaoyun X

Frontiers in oncology 20200528

Simultaneous identification of multiple single genes and multi-gene prognostic signatures with higher efficacy in liver cancer has rarely been reported. Here, 1,173 genes potentially related to the liver cancer prognosis were mined with Coremine, and the gene expression and survival data in 370 samples for overall survival (OS) and 319 samples for disease-free survival (DFS) were retrieved from The Cancer Genome Atlas. Numerous survival analyses results revealed that 39 genes and 28 genes signif ...[more]

PMID: 32547951

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Project description:BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, 'pathways in cancer,' was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982-0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, 'pathways in cancer.' The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

Project description:Background: The potential biological processes and laws of the biological components in malignant tumors can be understood more systematically and comprehensively through multi-omics analysis. This study elaborately explored the role of lipid metabolism in the prognosis of colorectal cancer (CRC) from the metabonomics and transcriptomics. Methods: We performed K-means unsupervised clustering algorithm and t test to identify the differential lipid metabolites determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) in the serum of 236 CRC patients of the First Hospital of Jilin University (JLUFH). Cox regression analysis was used to identify prognosis-associated lipid metabolites and to construct multi-lipid-metabolite prognostic signature. The composite nomogram composed of independent prognostic factors was utilized to individually predict the outcome of CRC patients. Glycerophospholipid metabolism was the most significant enrichment pathway for lipid metabolites in CRC, whose related hub genes (GMRHGs) were distinguished by gene set variation analysis (GSVA) and weighted gene co-expression network analysis (WGCNA). Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were utilized to develop the prognostic signature. Results: Six-lipid-metabolite and five-GMRHG prognostic signatures were developed, indicating favorable survival stratification effects on CRC patients. Using the independent prognostic factors as variables, we established a composite nomogram to individually evaluate the prognosis of CRC patients. The AUCs of one-, three-, and five-year ROC curves were 0.815, 0.815, and 0.805, respectively, showing auspicious prognostic accuracy. Furthermore, we explored the potential relationship between tumor microenvironment (TME) and immune infiltration. Moreover, the mutational frequency of TP53 in the high-risk group was significantly higher than that in the low-risk group (p < 0.001), while in the coordinate mutational status of TP53, the overall survival of CRC patients in the high-risk group was significantly lower than that in low-risk group with statistical differences. Conclusion: We identified the significance of lipid metabolism for the prognosis of CRC from the aspects of metabonomics and transcriptomics, which can provide a novel perspective for promoting individualized treatment and revealing the potential molecular biological characteristics of CRC. The composite nomogram including a six-lipid-metabolite prognostic signature is a promising predictor of the prognosis of CRC patients.

Dataset Information

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer.

Publications

Big Data-Based Identification of Multi-Gene Prognostic Signatures in Liver Cancer.

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