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. 2021 Jul 8;21(1):791.
doi: 10.1186/s12885-021-08520-1.

Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer

Shiyong Gao  1   2 , Jian Gang  3   4 , Miao Yu  3   4 , Guosong Xin  3   4 , Huixin Tan  5
Affiliations

Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer

Shiyong Gao et al. BMC Cancer. .

Abstract

Background: Liver cancer is the sixth most commonly diagnosed cancer and the fourth most common cause of cancer death. The purpose of this work is to find new diagnostic biomarkers or prognostic biomarkers and explore the biological functions related to the prognosis of liver cancer.

Methods: GSE25097 datasets were firstly obtained and compared with TCGA LICA datasets and an analysis of the overlapping differentially expressed genes (DEGs) was conducted. Cytoscape was used to screen out the Hub Genes among the DEGs. ROC curve analysis was used to screen the Hub Genes to determine the genes that could be used as diagnostic biomarkers. Kaplan-Meier analysis and Cox proportional hazards model screened genes associated with prognosis biomarkers, and further Gene Set Enrichment Analysis was performed on the prognosis genes to explore the mechanism affecting the survival and prognosis of liver cancer patients.

Results: 790 DEGs and 2162 DEGs were obtained respectively from the GSE25097 and TCGA LIHC data sets, and 102 Common DEGs were identified by overlapping the two DEGs. Further screening identified 22 Hub Genes from 102 Common DEGs. ROC and survival curves were used to analyze these 22 Hub Genes and it was found that there were 16 genes with a value of AUC > 90%. Among these, the expression levels of ESR1,SPP1 and FOSB genes were closely related to the survival time of liver cancer patients. Three common pathways of ESR1, FOBS and SPP1 genes were identified along with seven common pathways of ESR1 and SPP1 genes and four common pathways of ESR1 and FOSB genes.

Conclusions: SPP1, AURKA, NUSAP1, TOP2A, UBE2C, AFP, GMNN, PTTG1, RRM2, SPARCL1, CXCL12, FOS, DCN, SOCS3, FOSB and PCK1 can be used as diagnostic biomarkers for liver cancer, among which FOBS and SPP1 genes can also be used as prognostic biomarkers. Activation of the cell cycle-related pathway, pancreas beta cells pathway, and the estrogen signaling pathway, while on the other hand inhibition of the hallmark heme metabolism pathway, hallmark coagulation pathway, and the fat metabolism pathway may promote prognosis in liver cancer patients.

Keywords: Bioinformatics; Biomarker; Diagnostic biomarker; Liver cancer; Prognostic biomarker.

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Conflict of interest statement

All authors declare that they have no conflict of interests.

Figures

Fig. 1
Fig. 1
Venn diagram of DEGs of GSE25097 and TCGA LIHC datasets
Fig. 2
Fig. 2
Identification of DEGs of GSE25097 and TCGA LIHC datasets, adj.p-value < 0.05 and |logFC| > 2 were used as the cut off criteria. LIHC: liver cancer; TCGA: The Cancer Genome Atlas. A. Volcano map of DEGs obtained from the GSE25097 dataset B. Heap map of DEGs obtained from the GSE25097 dataset C. Volcano map of DEGs obtained from the TCGA LIHC dataset D. Heat map of DEGs obtained from the TCGA LIHC dataset
Fig. 3
Fig. 3
Enrichment analysis diagram of differentially expressed genes DEGs. A.GO analysis. B. Reactome analysis
Fig. 4
Fig. 4
PPI network diagram drawn by String. a. PPI network map of 102 DEGs. b. PPI network map of 22 Hub Genes
Fig. 5
Fig. 5
Expression levels of 22 Hub Gene. A. genes that is highly expressed in liver cancer. (a) SPP1, (b) AURKA, (c) NQO1, (d) NUSAP1, (e) TOP2A, (f) UBE2C, (g) AFP, (h) GMNN, (i) PTTG1, (j) RRM2, (k) UBE2T, (l) GPC3, (m) SPARCL1 in Normal Liver versus Liver Cancer tissues. B. genes that is lowly expressed in liver cancer. (n) ESR1, (o) CXCL12, (p) FOS, (q) DCN, (r) EGR1, (s) SOCS3, (t) CYP1A2, (u) FOSB, (v) PCK1 in Normal Liver versus Liver Cancer tissues
Fig. 6
Fig. 6
ROC curve of Hub Gene. a ESR1, (b)SPP1, (c) AURKA, (d)CXCL12, (e) FOS, (f) NQO1, (g) NUSAP1, (h)TOP2A, (i)UBE2C, (j) AFP, (k) DCN, (l) EGR1, (m) GMNN, (n)PTTG1, (o)RRM2, (p)SOCS3, (q)UBE2T, (r)CYP1A2, (s)GPC3, (t) FOSB, (u)PCK1, (v)SPARCL1
Fig. 7
Fig. 7
Survival analysis of 22 Hub Genes: (a) ESR1, (b) SPP1, (c) AURKA (d) CXCL12, (e) FOS, (f) NQO1, (g) NUSAP1, (h) TOP2A, (i) UBE2C, (j) AFP, (k) DCN, (l) EGR1, (m) GMNN, (n) PTTG1, (o) RRM2, (p) SOCS3, (q) UBE2T, (r) CYP1A2, (s) GPC3, (t) FOSB, (u) PCK1, (v) SPARCL1; p < 0.05 was considered as statistically significant
Fig. 8
Fig. 8
Identification of the enriched gene sets with GSEA analysis focused on a single gene as a phenotype. A.dot plot. B.curve graph. a1,b1 and c1 are the common pathways obtained by enrichment of ESR1, FOSB and SSP1 genes, respectively; a2 and c2 are the common pathways obtained by enrichment of ESR1 and SSP1 genes, respectively; a3 and b3 are the common pathways obtained by enrichment of ESR1 and FOSB genes, respectively
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