Data article on genes that share similar expression patterns with EEF1 complex proteins in hepatocellular carcinoma.

Eukaryotic Elongation Factor complex 1 (EEF1) consists of six subunits namely EEF1A1, EEF1A2, EEF1B2, EEF1D, EEF1E1 and EEF1G. Recently we showed that EEF1 complex proteins might play critical roles in cancer [1]. This article provides data on genes that share similar expression patterns with EEF1 complex proteins in cancer by analyzing RNA expression data using GEPIA online tool. Correlation analysis was performed on selected genes in a pairwise manner and the Pearson coefficients were automatically calculated by the GEPIA online tool. These data can be useful for future studies directed towards understanding the mechanisms by which EEF1 complex proteins affect in cancer pathogenesis.

Data description

The datasets that were analyzed in this data article were acquired by the GEPIA tool from the data generated by The Cancer Genome Atlas (TCGA) Research Network (dataset ID: TCGA.LIHC.sampleMap/HiSeqV2) [2,3]. For each EEF1 complex protein, a list of genes with similar expression to the input gene were created and ranked according to their Pearson coefficients. The first 50 genes were selected for each EEF1 protein (Table 1). Analysis of gene expression correlation between selected gene pairs is given in Fig. 1.

Table 1

Genes that share similar expression patterns with EEF1 complex proteins in cancer ranked by Pearson correlation coefficient (PCC).

EEF1A1			EEF1A2			EEF1B2
Gene symbol	Gene ID	PCC	Gene symbol	Gene ID	PCC	Gene symbol	Gene ID	PCC
EEF1A1P5	ENSG00000196205.8	0.72	CDK5R2	ENSG00000171450.5	0.74	RPS3A	ENSG00000145425.9	0.68
RPL5	ENSG00000122406.12	0.72	SYP	ENSG00000102003.10	0.74	RPS13	ENSG00000110700.6	0.66
EEF1A1P6	ENSG00000233476.3	0.69	ATP1A3	ENSG00000105409.15	0.69	RPS23	ENSG00000186468.12	0.66
EEF1A1P9	ENSG00000249264.1	0.68	FAIM2	ENSG00000135472.8	0.69	RPS18	ENSG00000231500.6	0.66
TPT1	ENSG00000133112.16	0.68	SYT5	ENSG00000129990.14	0.68	RPS27A	ENSG00000143947.12	0.65
EIF4B	ENSG00000063046.17	0.68	CPLX2	ENSG00000145920.14	0.68	RPL10A	ENSG00000198755.10	0.65
RPL3	ENSG00000100316.15	0.68	TMEM179	ENSG00000258986.6	0.68	RPS6	ENSG00000137154.12	0.64
RPL10A	ENSG00000198755.10	0.68	FAM163B	ENSG00000196990.8	0.68	RPLP0	ENSG00000089157.15	0.64
RPS3A	ENSG00000145425.9	0.68	RUNDC3A	ENSG00000108309.12	0.67	RPS15A	ENSG00000134419.15	0.64
IGBP1	ENSG00000089289.15	0.68	DUSP26	ENSG00000133878.8	0.67	RPS7	ENSG00000171863.12	0.63
EEF1G	ENSG00000254772.9	0.66	STMN2	ENSG00000104435.13	0.67	RPL5	ENSG00000122406.12	0.63
EEF1A1P12	ENSG00000214199.3	0.66	TAGLN3	ENSG00000144834.12	0.66	EEF1B2P3	ENSG00000232472.1	0.63
RPL31	ENSG00000071082.10	0.65	GS1-72M22.1	ENSG00000272163.1	0.66	RPL26	ENSG00000161970.12	0.62
RPS23	ENSG00000186468.12	0.65	UNC5A	ENSG00000113763.10	0.66	RPL31	ENSG00000071082.10	0.61
RPS4X	ENSG00000198034.10	0.65	PTPRN	ENSG00000054356.13	0.66	RPS8	ENSG00000142937.11	0.61
RPL26	ENSG00000161970.12	0.64	APLP1	ENSG00000105290.11	0.65	RPL27	ENSG00000131469.12	0.61
RPS13	ENSG00000110700.6	0.63	CELF4	ENSG00000101489.18	0.65	RPL36A	ENSG00000241343.9	0.6
NACA	ENSG00000196531.10	0.63	INA	ENSG00000148798.9	0.65	EEF1G	ENSG00000254772.9	0.6
LRRC75A-AS1	ENSG00000175061.17	0.63	CCDC184	ENSG00000177875.4	0.65	RPS14	ENSG00000164587.11	0.6
NPM1	ENSG00000181163.13	0.63	TMEM130	ENSG00000166448.14	0.65	RPS29	ENSG00000213741.8	0.6
RPL4	ENSG00000174444.14	0.62	HCN1	ENSG00000164588.4	0.64	RPSA	ENSG00000168028.13	0.59
RPL34	ENSG00000109475.16	0.62	RIMBP2	ENSG00000060709.13	0.64	RPL39	ENSG00000198918.7	0.59
HNRNPA1	ENSG00000135486.17	0.61	SPTBN4	ENSG00000160460.15	0.64	RPL21	ENSG00000122026.10	0.59
RPS25	ENSG00000118181.10	0.61	BEX1	ENSG00000133169.5	0.64	RP11-572P18.1	ENSG00000220842.6	0.59
EIF3L	ENSG00000100129.17	0.61	CAMK2B	ENSG00000058404.19	0.64	RPL7A	ENSG00000148303.16	0.59
GNB2L1	ENSG00000204628.11	0.6	SYT4	ENSG00000132872.11	0.64	RPS12	ENSG00000112306.7	0.57
EIF4BP7	ENSG00000225031.1	0.6	ST8SIA3	ENSG00000177511.5	0.63	RPS10	ENSG00000124614.13	0.57
RPL7A	ENSG00000148303.16	0.6	PDZD7	ENSG00000186862.17	0.63	RPL4	ENSG00000174444.14	0.57
RPS15A	ENSG00000134419.15	0.6	CHRNB2	ENSG00000160716.4	0.63	RPS5	ENSG00000083845.8	0.56
BTF3	ENSG00000145741.15	0.6	SEZ6L2	ENSG00000174938.14	0.63	EEF1A1	ENSG00000156508.17	0.56
NSA2	ENSG00000164346.9	0.6	GPR22	ENSG00000172209.5	0.63	RPS3	ENSG00000149273.14	0.56
RSL24D1	ENSG00000137876.9	0.6	NAP1L5	ENSG00000177432.6	0.63	RPL37A	ENSG00000197756.9	0.55
EEF1A1P19	ENSG00000249855.1	0.6	MAPK8IP2	ENSG00000008735.13	0.62	RPS17	ENSG00000182774.10	0.55
RPS27A	ENSG00000143947.12	0.59	KIF1A	ENSG00000130294.14	0.62	RPS24	ENSG00000138326.18	0.55
RPS14	ENSG00000164587.11	0.59	RALYL	ENSG00000184672.11	0.62	RPS11	ENSG00000142534.6	0.55
RPL41	ENSG00000279483.1	0.59	BEGAIN	ENSG00000183092.15	0.62	RPL14	ENSG00000188846.13	0.55
RPL15	ENSG00000174748.18	0.58	DYNC1I1	ENSG00000158560.14	0.62	RPL7	ENSG00000147604.13	0.55
CCNI	ENSG00000118816.9	0.58	SYNGR3	ENSG00000127561.14	0.62	RPS4X	ENSG00000198034.10	0.55
RPS6	ENSG00000137154.12	0.58	CALY	ENSG00000130643.8	0.62	EIF3E	ENSG00000104408.9	0.55
RPL10	ENSG00000147403.16	0.58	JPH4	ENSG00000092051.16	0.61	RPL17	ENSG00000265681.6	0.55
RPL11	ENSG00000142676.12	0.57	SCAMP5	ENSG00000198794.11	0.61	RPL10	ENSG00000147403.16	0.54
RPL14	ENSG00000188846.13	0.57	UCHL1	ENSG00000154277.12	0.61	RPS9	ENSG00000170889.13	0.54
RPL17	ENSG00000265681.6	0.57	RAB3C	ENSG00000152932.7	0.61	BTF3	ENSG00000145741.15	0.54
RPS8	ENSG00000142937.11	0.57	SCN3B	ENSG00000166257.8	0.61	RPL29	ENSG00000162244.10	0.54
RPL12	ENSG00000197958.12	0.57	FBLL1	ENSG00000188573.7	0.6	RPL34	ENSG00000109475.16	0.54
EEF1B2	ENSG00000114942.13	0.56	TERF2IP	ENSG00000166848.5	0.6	RPL12	ENSG00000197958.12	0.54
RPL22	ENSG00000116251.9	0.56	SYT1	ENSG00000067715.13	0.6	RPS21	ENSG00000171858.17	0.54
RPL24	ENSG00000114391.12	0.56	SLC8A2	ENSG00000118160.13	0.6	RPL27A	ENSG00000166441.12	0.54
EIF3E	ENSG00000104408.9	0.56	RIPPLY2	ENSG00000203877.7	0.6	RPL11	ENSG00000142676.12	0.53
EEF1A1P7	ENSG00000268222.1	0.56	MAP6	ENSG00000171533.11	0.6	HNRNPA1	ENSG00000135486.17	0.53

Fig. 1

Analysis of gene expression correlation between selected gene pairs using datasets.

Experimental design, materials, and methods

Identification of similarly expressed genes

In order to identify genes that share similar expression patterns with EEF1 complex proteins, GEPIA analysis was performed. GEPIA is an online data repository that uses RNA-Seq datasets based on the UCSC Xena project and allows protein expression analysis from genome-wide DNA and RNA sequencing data (http://gepia.cancer-pku.cn/index.html) [4,5]. First, EEF1A1, EEF1A2, EEF1B2, EEF1D, EEF1E1 or EEF1G were selected as the gene of interest under Similar Genes section. Then, the number of genes was set to 50 and the cancer type was set as TCGA Tumor-liver hepatocellular carcinoma. Pearson correlation coefficients (PCCs) were automatically calculated by the GEPIA online tool.

Correlation analysis

The similarity in expression patterns was further evaluated and validated by the Correlation Analysis feature of GEPIA tool. For this purpose, genes of interest were set as gene A and gene B. The tool offers statistical analysis based on methods including Pearson, Spearman and Kendall; and uses non-log scale for calculation and uses the log-scale axis for visualization. Among these, Pearson correlation was selected and further confirmed by analyzing the Spearman correlations. The cancer type was set as liver hepatocellular carcinoma.

Specifications Table

Subject	Cancer research
Specific subject area	Protein expression in cancer
Type of data	RawTableFigure
How data were acquired	GEPIA online tool
Data format	Raw and analyzed
Parameters for data collection	RNA expression levels
Description of data collection	By analyzing RNA expression data using online tools
Data source location	The data published here are based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga).
Data accessibility	The raw data files are provided by the TCGA Research Network and can be reached at https://xenabrowser.net/datapages/?dataset=TCGA.LIHC.sampleMap%2FHiSeqV2&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443.Analyzed data are shared with this article.
Related research article	Biterge-Sut B. 2019. Alterations in Eukaryotic Elongation Factor complex proteins (EEF1s) in cancer and their implications in epigenetic regulation. Life Sci. 238:116977. https://doi.org/10.1016/j.lfs.2019.116977

Value of the Data• Cancer pathogenesis relies largely on aberrant protein expression. • EEF1 complex proteins, together with other genes that share similar expression patterns with them, could affect carcinogenesis [1]. • These data can be useful for future studies directed towards understanding the mechanisms by which EEF1 complex proteins affect in cancer.